Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations22236
Missing cells53156
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory312.0 B

Variable types

Numeric19
Text12
DateTime1
Categorical7

Alerts

Season is highly overall correlated with season_idHigh correlation
actual_weight is highly overall correlated with horse_numberHigh correlation
finishing_position is highly overall correlated with recent_ave_rank and 4 other fieldsHigh correlation
horse_number is highly overall correlated with actual_weightHigh correlation
jockey_ave_rank is highly overall correlated with win_oddsHigh correlation
race_course is highly overall correlated with race_course_id and 2 other fieldsHigh correlation
race_course_id is highly overall correlated with race_course and 2 other fieldsHigh correlation
recent_ave_rank is highly overall correlated with finishing_position and 1 other fieldsHigh correlation
running_position_1 is highly overall correlated with running_position_2 and 1 other fieldsHigh correlation
running_position_2 is highly overall correlated with running_position_1 and 1 other fieldsHigh correlation
running_position_3 is highly overall correlated with finishing_position and 2 other fieldsHigh correlation
running_position_4 is highly overall correlated with finishing_positionHigh correlation
running_position_5 is highly overall correlated with finishing_positionHigh correlation
running_position_6 is highly overall correlated with finishing_position and 2 other fieldsHigh correlation
season_id is highly overall correlated with SeasonHigh correlation
track is highly overall correlated with race_course and 2 other fieldsHigh correlation
track_condition is highly overall correlated with track_condition_idHigh correlation
track_condition_id is highly overall correlated with track_conditionHigh correlation
track_id is highly overall correlated with race_course and 2 other fieldsHigh correlation
win_odds is highly overall correlated with jockey_ave_rank and 1 other fieldsHigh correlation
track_condition is highly imbalanced (53.2%) Imbalance
length_behind_winner has 1898 (8.5%) missing values Missing
running_position_4 has 10011 (45.0%) missing values Missing
running_position_5 has 19435 (87.4%) missing values Missing
running_position_6 has 21812 (98.1%) missing values Missing

Reproduction

Analysis started2025-01-15 12:33:48.981437
Analysis finished2025-01-15 12:35:24.655961
Duration1 minute and 35.67 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

finishing_position
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3829826
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:24.760560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.417218
Coefficient of variation (CV)0.53536383
Kurtosis-1.1978932
Mean6.3829826
Median Absolute Deviation (MAD)3
Skewness0.030521852
Sum141932
Variance11.677379
MonotonicityNot monotonic
2025-01-15T18:05:24.946752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1918
8.6%
4 1907
8.6%
3 1899
8.5%
2 1896
8.5%
5 1891
8.5%
6 1890
8.5%
7 1887
8.5%
8 1877
8.4%
9 1857
8.4%
10 1822
8.2%
Other values (2) 3392
15.3%
ValueCountFrequency (%)
1 1918
8.6%
2 1896
8.5%
3 1899
8.5%
4 1907
8.6%
5 1891
8.5%
6 1890
8.5%
7 1887
8.5%
8 1877
8.4%
9 1857
8.4%
10 1822
8.2%
ValueCountFrequency (%)
12 1632
7.3%
11 1760
7.9%
10 1822
8.2%
9 1857
8.4%
8 1877
8.4%
7 1887
8.5%
6 1890
8.5%
5 1891
8.5%
4 1907
8.6%
3 1899
8.5%

horse_number
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8175931
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:25.113541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7346442
Coefficient of variation (CV)0.54779511
Kurtosis-1.1111679
Mean6.8175931
Median Absolute Deviation (MAD)3
Skewness0.099011654
Sum151596
Variance13.947567
MonotonicityNot monotonic
2025-01-15T18:05:25.302374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1813
 
8.2%
2 1808
 
8.1%
6 1800
 
8.1%
4 1775
 
8.0%
5 1773
 
8.0%
3 1769
 
8.0%
7 1747
 
7.9%
8 1741
 
7.8%
9 1713
 
7.7%
10 1686
 
7.6%
Other values (4) 4611
20.7%
ValueCountFrequency (%)
1 1813
8.2%
2 1808
8.1%
3 1769
8.0%
4 1775
8.0%
5 1773
8.0%
6 1800
8.1%
7 1747
7.9%
8 1741
7.8%
9 1713
7.7%
10 1686
7.6%
ValueCountFrequency (%)
14 636
 
2.9%
13 703
 
3.2%
12 1626
7.3%
11 1646
7.4%
10 1686
7.6%
9 1713
7.7%
8 1741
7.8%
7 1747
7.9%
6 1800
8.1%
5 1773
8.0%
Distinct2108
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:25.758692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length14
Mean length12.187039
Min length3

Characters and Unicode

Total characters270991
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)0.8%

Sample

1st rowCAREFREE LET GO
2nd rowVERY RICH MAN
3rd rowFANTASTIC KAKA
4th rowVICTORY MAGIC
5th rowEXCITING DREAM
ValueCountFrequency (%)
happy 470
 
1.1%
dragon 458
 
1.0%
star 443
 
1.0%
the 427
 
1.0%
king 425
 
1.0%
lucky 416
 
0.9%
of 372
 
0.8%
super 327
 
0.7%
win 325
 
0.7%
boy 321
 
0.7%
Other values (1921) 40657
91.1%
2025-01-15T18:05:26.461886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 26041
 
9.6%
22405
 
8.3%
A 20713
 
7.6%
R 19661
 
7.3%
I 18022
 
6.7%
N 17984
 
6.6%
O 17443
 
6.4%
T 14687
 
5.4%
L 13574
 
5.0%
S 13435
 
5.0%
Other values (19) 87026
32.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 247874
91.5%
Space Separator 22405
 
8.3%
Other Punctuation 573
 
0.2%
Dash Punctuation 139
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 26041
 
10.5%
A 20713
 
8.4%
R 19661
 
7.9%
I 18022
 
7.3%
N 17984
 
7.3%
O 17443
 
7.0%
T 14687
 
5.9%
L 13574
 
5.5%
S 13435
 
5.4%
G 9331
 
3.8%
Other values (16) 76983
31.1%
Space Separator
ValueCountFrequency (%)
22405
100.0%
Other Punctuation
ValueCountFrequency (%)
' 573
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 247874
91.5%
Common 23117
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 26041
 
10.5%
A 20713
 
8.4%
R 19661
 
7.9%
I 18022
 
7.3%
N 17984
 
7.3%
O 17443
 
7.0%
T 14687
 
5.9%
L 13574
 
5.5%
S 13435
 
5.4%
G 9331
 
3.8%
Other values (16) 76983
31.1%
Common
ValueCountFrequency (%)
22405
96.9%
' 573
 
2.5%
- 139
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 26041
 
9.6%
22405
 
8.3%
A 20713
 
7.6%
R 19661
 
7.3%
I 18022
 
6.7%
N 17984
 
6.6%
O 17443
 
6.4%
T 14687
 
5.4%
L 13574
 
5.0%
S 13435
 
5.0%
Other values (19) 87026
32.1%
Distinct2108
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:26.985872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters88944
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)0.8%

Sample

1st rowT059
2nd rowV286
3rd rowP363
4th rowT272
5th rowP191
ValueCountFrequency (%)
p293 39
 
0.2%
n409 39
 
0.2%
s023 39
 
0.2%
n317 38
 
0.2%
s205 38
 
0.2%
t099 37
 
0.2%
p272 37
 
0.2%
n432 35
 
0.2%
s138 35
 
0.2%
n265 34
 
0.2%
Other values (2098) 21865
98.3%
2025-01-15T18:05:27.683680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10155
11.4%
2 9746
11.0%
3 9447
10.6%
0 9130
10.3%
4 6670
 
7.5%
S 5489
 
6.2%
T 5200
 
5.8%
8 4538
 
5.1%
5 4395
 
4.9%
9 4367
 
4.9%
Other values (11) 19807
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66708
75.0%
Uppercase Letter 22236
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 5489
24.7%
T 5200
23.4%
P 4155
18.7%
V 3054
13.7%
N 2207
9.9%
A 936
 
4.2%
M 794
 
3.6%
L 274
 
1.2%
K 114
 
0.5%
J 7
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 10155
15.2%
2 9746
14.6%
3 9447
14.2%
0 9130
13.7%
4 6670
10.0%
8 4538
6.8%
5 4395
6.6%
9 4367
6.5%
6 4228
6.3%
7 4032
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66708
75.0%
Latin 22236
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 5489
24.7%
T 5200
23.4%
P 4155
18.7%
V 3054
13.7%
N 2207
9.9%
A 936
 
4.2%
M 794
 
3.6%
L 274
 
1.2%
K 114
 
0.5%
J 7
 
< 0.1%
Common
ValueCountFrequency (%)
1 10155
15.2%
2 9746
14.6%
3 9447
14.2%
0 9130
13.7%
4 6670
10.0%
8 4538
6.8%
5 4395
6.6%
9 4367
6.5%
6 4228
6.3%
7 4032
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10155
11.4%
2 9746
11.0%
3 9447
10.6%
0 9130
10.3%
4 6670
 
7.5%
S 5489
 
6.2%
T 5200
 
5.8%
8 4538
 
5.1%
5 4395
 
4.9%
9 4367
 
4.9%
Other values (11) 19807
22.3%

jockey
Text

Distinct103
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:27.990498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.2985249
Min length5

Characters and Unicode

Total characters184526
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.1%

Sample

1st rowM L Yeung
2nd rowU Rispoli
3rd rowB Prebble
4th rowJ Moreira
5th rowH Bowman
ValueCountFrequency (%)
k 4478
 
8.5%
c 4169
 
7.9%
n 2701
 
5.1%
h 2520
 
4.8%
m 2344
 
4.4%
y 1745
 
3.3%
j 1551
 
2.9%
t 1545
 
2.9%
moreira 1534
 
2.9%
d 1367
 
2.6%
Other values (117) 28947
54.7%
2025-01-15T18:05:28.494997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30665
16.6%
e 15313
 
8.3%
o 10800
 
5.9%
n 9233
 
5.0%
a 9036
 
4.9%
r 7855
 
4.3%
i 7620
 
4.1%
C 7601
 
4.1%
l 7571
 
4.1%
u 5327
 
2.9%
Other values (42) 73505
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101240
54.9%
Uppercase Letter 52615
28.5%
Space Separator 30665
 
16.6%
Other Punctuation 4
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15313
15.1%
o 10800
10.7%
n 9233
9.1%
a 9036
8.9%
r 7855
7.8%
i 7620
 
7.5%
l 7571
 
7.5%
u 5327
 
5.3%
g 4806
 
4.7%
t 4408
 
4.4%
Other values (15) 19271
19.0%
Uppercase Letter
ValueCountFrequency (%)
C 7601
14.4%
M 4887
 
9.3%
K 4483
 
8.5%
H 3631
 
6.9%
L 3549
 
6.7%
N 3317
 
6.3%
T 3259
 
6.2%
W 3058
 
5.8%
Y 2761
 
5.2%
S 2699
 
5.1%
Other values (14) 13370
25.4%
Space Separator
ValueCountFrequency (%)
30665
100.0%
Other Punctuation
ValueCountFrequency (%)
' 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 153855
83.4%
Common 30671
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15313
 
10.0%
o 10800
 
7.0%
n 9233
 
6.0%
a 9036
 
5.9%
r 7855
 
5.1%
i 7620
 
5.0%
C 7601
 
4.9%
l 7571
 
4.9%
u 5327
 
3.5%
M 4887
 
3.2%
Other values (39) 68612
44.6%
Common
ValueCountFrequency (%)
30665
> 99.9%
' 4
 
< 0.1%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30665
16.6%
e 15313
 
8.3%
o 10800
 
5.9%
n 9233
 
5.0%
a 9036
 
4.9%
r 7855
 
4.3%
i 7620
 
4.1%
C 7601
 
4.1%
l 7571
 
4.1%
u 5327
 
2.9%
Other values (42) 73505
39.8%
Distinct87
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:28.734050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length13
Mean length7.6900971
Min length4

Characters and Unicode

Total characters170997
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)0.2%

Sample

1st rowC S Shum
2nd rowT K Ng
3rd rowL Ho
4th rowJ Moore
5th rowJ Moore
ValueCountFrequency (%)
c 4409
 
7.6%
s 3977
 
6.8%
a 3546
 
6.1%
j 3173
 
5.4%
p 2816
 
4.8%
w 2696
 
4.6%
d 2492
 
4.3%
t 2447
 
4.2%
k 2344
 
4.0%
y 2287
 
3.9%
Other values (98) 28073
48.2%
2025-01-15T18:05:29.164011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36024
21.1%
i 9136
 
5.3%
S 8484
 
5.0%
u 8354
 
4.9%
C 7277
 
4.3%
r 7161
 
4.2%
o 7081
 
4.1%
e 6353
 
3.7%
n 5729
 
3.4%
a 5546
 
3.2%
Other values (39) 69852
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74944
43.8%
Uppercase Letter 59143
34.6%
Space Separator 36024
21.1%
Other Punctuation 883
 
0.5%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9136
12.2%
u 8354
11.1%
r 7161
9.6%
o 7081
9.4%
e 6353
8.5%
n 5729
7.6%
a 5546
7.4%
l 5395
7.2%
s 4637
 
6.2%
z 3550
 
4.7%
Other values (14) 12002
16.0%
Uppercase Letter
ValueCountFrequency (%)
S 8484
14.3%
C 7277
12.3%
Y 5468
 
9.2%
T 3684
 
6.2%
A 3548
 
6.0%
L 3437
 
5.8%
F 3244
 
5.5%
M 3240
 
5.5%
J 3173
 
5.4%
W 3068
 
5.2%
Other values (12) 14520
24.6%
Space Separator
ValueCountFrequency (%)
36024
100.0%
Other Punctuation
ValueCountFrequency (%)
' 883
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 134087
78.4%
Common 36910
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9136
 
6.8%
S 8484
 
6.3%
u 8354
 
6.2%
C 7277
 
5.4%
r 7161
 
5.3%
o 7081
 
5.3%
e 6353
 
4.7%
n 5729
 
4.3%
a 5546
 
4.1%
Y 5468
 
4.1%
Other values (36) 63498
47.4%
Common
ValueCountFrequency (%)
36024
97.6%
' 883
 
2.4%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36024
21.1%
i 9136
 
5.3%
S 8484
 
5.0%
u 8354
 
4.9%
C 7277
 
4.3%
r 7161
 
4.2%
o 7081
 
4.1%
e 6353
 
3.7%
n 5729
 
3.4%
a 5546
 
3.2%
Other values (39) 69852
40.8%

actual_weight
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.97527
Minimum103
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:29.355361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile113
Q1118
median123
Q3128
95-th percentile133
Maximum133
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3426001
Coefficient of variation (CV)0.051576226
Kurtosis-0.67593283
Mean122.97527
Median Absolute Deviation (MAD)5
Skewness-0.26283927
Sum2734478
Variance40.228576
MonotonicityNot monotonic
2025-01-15T18:05:29.550779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
126 1542
 
6.9%
133 1468
 
6.6%
123 1258
 
5.7%
125 1243
 
5.6%
120 1162
 
5.2%
121 1093
 
4.9%
122 1058
 
4.8%
124 1041
 
4.7%
127 1040
 
4.7%
128 991
 
4.5%
Other values (21) 10340
46.5%
ValueCountFrequency (%)
103 13
 
0.1%
104 17
 
0.1%
105 34
 
0.2%
106 31
 
0.1%
107 60
 
0.3%
108 120
0.5%
109 110
 
0.5%
110 106
 
0.5%
111 264
1.2%
112 293
1.3%
ValueCountFrequency (%)
133 1468
6.6%
132 767
3.4%
131 986
4.4%
130 914
4.1%
129 899
4.0%
128 991
4.5%
127 1040
4.7%
126 1542
6.9%
125 1243
5.6%
124 1041
4.7%

declared_horse_weight
Real number (ℝ)

Distinct386
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1109.1501
Minimum902
Maximum1365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:29.762602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum902
5-th percentile1011
Q11067
median1107
Q31151
95-th percentile1211
Maximum1365
Range463
Interquartile range (IQR)84

Descriptive statistics

Standard deviation60.908008
Coefficient of variation (CV)0.054914125
Kurtosis-0.084782417
Mean1109.1501
Median Absolute Deviation (MAD)42
Skewness0.13911548
Sum24663062
Variance3709.7854
MonotonicityNot monotonic
2025-01-15T18:05:29.992766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1088 177
 
0.8%
1094 162
 
0.7%
1093 161
 
0.7%
1102 157
 
0.7%
1097 155
 
0.7%
1099 154
 
0.7%
1092 154
 
0.7%
1083 153
 
0.7%
1080 152
 
0.7%
1106 151
 
0.7%
Other values (376) 20660
92.9%
ValueCountFrequency (%)
902 1
 
< 0.1%
914 1
 
< 0.1%
916 2
< 0.1%
921 1
 
< 0.1%
922 1
 
< 0.1%
923 1
 
< 0.1%
924 1
 
< 0.1%
927 1
 
< 0.1%
930 3
< 0.1%
931 1
 
< 0.1%
ValueCountFrequency (%)
1365 1
< 0.1%
1326 1
< 0.1%
1325 1
< 0.1%
1324 1
< 0.1%
1322 1
< 0.1%
1316 1
< 0.1%
1315 2
< 0.1%
1311 1
< 0.1%
1308 1
< 0.1%
1306 2
< 0.1%

draw
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8107573
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:30.178835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.708308
Coefficient of variation (CV)0.54447806
Kurtosis-1.094741
Mean6.8107573
Median Absolute Deviation (MAD)3
Skewness0.10097137
Sum151444
Variance13.751548
MonotonicityNot monotonic
2025-01-15T18:05:30.365137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 1824
8.2%
5 1803
8.1%
7 1803
8.1%
3 1785
 
8.0%
6 1771
 
8.0%
4 1769
 
8.0%
9 1766
 
7.9%
8 1766
 
7.9%
1 1747
 
7.9%
10 1698
 
7.6%
Other values (4) 4504
20.3%
ValueCountFrequency (%)
1 1747
7.9%
2 1824
8.2%
3 1785
8.0%
4 1769
8.0%
5 1803
8.1%
6 1771
8.0%
7 1803
8.1%
8 1766
7.9%
9 1766
7.9%
10 1698
7.6%
ValueCountFrequency (%)
14 630
 
2.8%
13 680
 
3.1%
12 1560
7.0%
11 1634
7.3%
10 1698
7.6%
9 1766
7.9%
8 1766
7.9%
7 1803
8.1%
6 1771
8.0%
5 1803
8.1%

length_behind_winner
Text

Missing 

Distinct171
Distinct (%)0.8%
Missing1898
Missing (%)8.5%
Memory size173.8 KiB
2025-01-15T18:05:30.735456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.9237388
Min length1

Characters and Unicode

Total characters79801
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)0.2%

Sample

1st row2
2nd row3/4
3rd row7-1/4
4th row3-1/2
5th row4-3/4
ValueCountFrequency (%)
3-1/2 713
 
3.5%
2-3/4 707
 
3.5%
3 705
 
3.5%
4-1/4 705
 
3.5%
2-1/2 702
 
3.5%
2-1/4 687
 
3.4%
2 685
 
3.4%
3-3/4 675
 
3.3%
3-1/4 672
 
3.3%
4-1/2 657
 
3.2%
Other values (159) 13430
66.0%
2025-01-15T18:05:31.321265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 14762
18.5%
1 13914
17.4%
- 13841
17.3%
4 12483
15.6%
2 8231
10.3%
3 7954
10.0%
5 2183
 
2.7%
6 1691
 
2.1%
7 1269
 
1.6%
8 979
 
1.2%
Other values (12) 2494
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49805
62.4%
Other Punctuation 14762
 
18.5%
Dash Punctuation 13841
 
17.3%
Uppercase Letter 1391
 
1.7%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13914
27.9%
4 12483
25.1%
2 8231
16.5%
3 7954
16.0%
5 2183
 
4.4%
6 1691
 
3.4%
7 1269
 
2.5%
8 979
 
2.0%
9 671
 
1.3%
0 430
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
N 472
33.9%
H 373
26.8%
S 298
21.4%
D 130
 
9.3%
O 56
 
4.0%
E 55
 
4.0%
M 3
 
0.2%
L 3
 
0.2%
T 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 14762
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13841
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78410
98.3%
Latin 1391
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 14762
18.8%
1 13914
17.7%
- 13841
17.7%
4 12483
15.9%
2 8231
10.5%
3 7954
10.1%
5 2183
 
2.8%
6 1691
 
2.2%
7 1269
 
1.6%
8 979
 
1.2%
Other values (3) 1103
 
1.4%
Latin
ValueCountFrequency (%)
N 472
33.9%
H 373
26.8%
S 298
21.4%
D 130
 
9.3%
O 56
 
4.0%
E 55
 
4.0%
M 3
 
0.2%
L 3
 
0.2%
T 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 14762
18.5%
1 13914
17.4%
- 13841
17.3%
4 12483
15.6%
2 8231
10.3%
3 7954
10.0%
5 2183
 
2.7%
6 1691
 
2.1%
7 1269
 
1.6%
8 979
 
1.2%
Other values (12) 2494
 
3.1%

running_position_1
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7678989
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:31.512715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6845915
Coefficient of variation (CV)0.54442177
Kurtosis-1.0900357
Mean6.7678989
Median Absolute Deviation (MAD)3
Skewness0.10206122
Sum150491
Variance13.576214
MonotonicityNot monotonic
2025-01-15T18:05:31.701447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 1818
8.2%
4 1814
8.2%
3 1804
8.1%
6 1803
8.1%
5 1797
8.1%
1 1797
8.1%
9 1772
8.0%
2 1770
8.0%
8 1759
7.9%
10 1727
 
7.8%
Other values (4) 4375
19.7%
ValueCountFrequency (%)
1 1797
8.1%
2 1770
8.0%
3 1804
8.1%
4 1814
8.2%
5 1797
8.1%
6 1803
8.1%
7 1818
8.2%
8 1759
7.9%
9 1772
8.0%
10 1727
7.8%
ValueCountFrequency (%)
14 553
 
2.5%
13 678
 
3.0%
12 1527
6.9%
11 1617
7.3%
10 1727
7.8%
9 1772
8.0%
8 1759
7.9%
7 1818
8.2%
6 1803
8.1%
5 1797
8.1%

running_position_2
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7397913
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:31.883445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6683051
Coefficient of variation (CV)0.54427576
Kurtosis-1.092947
Mean6.7397913
Median Absolute Deviation (MAD)3
Skewness0.097941714
Sum149866
Variance13.456462
MonotonicityNot monotonic
2025-01-15T18:05:32.071732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 1833
8.2%
4 1818
8.2%
1 1816
8.2%
7 1806
8.1%
2 1791
8.1%
3 1789
8.0%
8 1789
8.0%
5 1786
8.0%
9 1773
8.0%
10 1722
 
7.7%
Other values (4) 4313
19.4%
ValueCountFrequency (%)
1 1816
8.2%
2 1791
8.1%
3 1789
8.0%
4 1818
8.2%
5 1786
8.0%
6 1833
8.2%
7 1806
8.1%
8 1789
8.0%
9 1773
8.0%
10 1722
7.7%
ValueCountFrequency (%)
14 488
 
2.2%
13 669
 
3.0%
12 1525
6.9%
11 1631
7.3%
10 1722
7.7%
9 1773
8.0%
8 1789
8.0%
7 1806
8.1%
6 1833
8.2%
5 1786
8.0%

running_position_3
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6084278
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:32.252250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5909813
Coefficient of variation (CV)0.54339419
Kurtosis-1.0993863
Mean6.6084278
Median Absolute Deviation (MAD)3
Skewness0.091969478
Sum146945
Variance12.895146
MonotonicityNot monotonic
2025-01-15T18:05:32.443223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1877
8.4%
5 1866
8.4%
6 1846
8.3%
7 1833
8.2%
3 1831
8.2%
8 1829
8.2%
4 1827
8.2%
2 1814
8.2%
9 1774
8.0%
10 1746
7.9%
Other values (4) 3993
18.0%
ValueCountFrequency (%)
1 1877
8.4%
2 1814
8.2%
3 1831
8.2%
4 1827
8.2%
5 1866
8.4%
6 1846
8.3%
7 1833
8.2%
8 1829
8.2%
9 1774
8.0%
10 1746
7.9%
ValueCountFrequency (%)
14 319
 
1.4%
13 466
 
2.1%
12 1532
6.9%
11 1676
7.5%
10 1746
7.9%
9 1774
8.0%
8 1829
8.2%
7 1833
8.2%
6 1846
8.3%
5 1866
8.4%

running_position_4
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)0.1%
Missing10011
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean6.4691207
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:32.623561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4721521
Coefficient of variation (CV)0.53672706
Kurtosis-1.1550728
Mean6.4691207
Median Absolute Deviation (MAD)3
Skewness0.049528228
Sum79085
Variance12.05584
MonotonicityNot monotonic
2025-01-15T18:05:32.805996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 1040
 
4.7%
6 1037
 
4.7%
1 1036
 
4.7%
7 1032
 
4.6%
4 1028
 
4.6%
9 1028
 
4.6%
5 1027
 
4.6%
2 1019
 
4.6%
8 1007
 
4.5%
10 999
 
4.5%
Other values (4) 1972
 
8.9%
(Missing) 10011
45.0%
ValueCountFrequency (%)
1 1036
4.7%
2 1019
4.6%
3 1040
4.7%
4 1028
4.6%
5 1027
4.6%
6 1037
4.7%
7 1032
4.6%
8 1007
4.5%
9 1028
4.6%
10 999
4.5%
ValueCountFrequency (%)
14 52
 
0.2%
13 81
 
0.4%
12 890
4.0%
11 949
4.3%
10 999
4.5%
9 1028
4.6%
8 1007
4.5%
7 1032
4.6%
6 1037
4.7%
5 1027
4.6%
Distinct3713
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:33.232707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters155652
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)4.7%

Sample

1st row1.09.33
2nd row1.10.53
3rd row1.36.13
4th row1.11.77
5th row1.23.02
ValueCountFrequency (%)
1.10.50 48
 
0.2%
1.10.64 47
 
0.2%
1.10.72 46
 
0.2%
1.10.42 44
 
0.2%
1.10.37 44
 
0.2%
1.10.43 43
 
0.2%
1.10.35 43
 
0.2%
1.10.45 43
 
0.2%
1.10.80 43
 
0.2%
1.10.82 42
 
0.2%
Other values (3703) 21793
98.0%
2025-01-15T18:05:33.848561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 44472
28.6%
1 32945
21.2%
0 14255
 
9.2%
2 11950
 
7.7%
4 9586
 
6.2%
3 9100
 
5.8%
5 8491
 
5.5%
9 7269
 
4.7%
8 6097
 
3.9%
7 5872
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111180
71.4%
Other Punctuation 44472
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32945
29.6%
0 14255
12.8%
2 11950
 
10.7%
4 9586
 
8.6%
3 9100
 
8.2%
5 8491
 
7.6%
9 7269
 
6.5%
8 6097
 
5.5%
7 5872
 
5.3%
6 5615
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 44472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155652
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 44472
28.6%
1 32945
21.2%
0 14255
 
9.2%
2 11950
 
7.7%
4 9586
 
6.2%
3 9100
 
5.8%
5 8491
 
5.5%
9 7269
 
4.7%
8 6097
 
3.9%
7 5872
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 44472
28.6%
1 32945
21.2%
0 14255
 
9.2%
2 11950
 
7.7%
4 9586
 
6.2%
3 9100
 
5.8%
5 8491
 
5.5%
9 7269
 
4.7%
8 6097
 
3.9%
7 5872
 
3.8%

win_odds
Real number (ℝ)

High correlation 

Distinct180
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.290889
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:34.088432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q17.4
median15
Q337
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)29.6

Descriptive statistics

Standard deviation30.20104
Coefficient of variation (CV)1.0675182
Kurtosis0.61785955
Mean28.290889
Median Absolute Deviation (MAD)9.7
Skewness1.3952594
Sum629076.2
Variance912.1028
MonotonicityNot monotonic
2025-01-15T18:05:34.320638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 2131
 
9.6%
10 732
 
3.3%
11 680
 
3.1%
12 671
 
3.0%
13 595
 
2.7%
15 530
 
2.4%
14 514
 
2.3%
16 487
 
2.2%
17 444
 
2.0%
18 361
 
1.6%
Other values (170) 15091
67.9%
ValueCountFrequency (%)
1 2
 
< 0.1%
1.1 2
 
< 0.1%
1.2 6
 
< 0.1%
1.3 6
 
< 0.1%
1.4 9
 
< 0.1%
1.5 17
 
0.1%
1.6 38
0.2%
1.7 31
0.1%
1.8 46
0.2%
1.9 50
0.2%
ValueCountFrequency (%)
99 2131
9.6%
98 32
 
0.1%
97 26
 
0.1%
96 28
 
0.1%
95 32
 
0.1%
94 32
 
0.1%
93 30
 
0.1%
92 30
 
0.1%
91 25
 
0.1%
90 33
 
0.1%

running_position_5
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)0.5%
Missing19435
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean6.3127454
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:34.505673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.404096
Coefficient of variation (CV)0.53924177
Kurtosis-1.1668498
Mean6.3127454
Median Absolute Deviation (MAD)3
Skewness0.059474826
Sum17682
Variance11.58787
MonotonicityNot monotonic
2025-01-15T18:05:34.688756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 252
 
1.1%
2 250
 
1.1%
8 248
 
1.1%
7 245
 
1.1%
1 245
 
1.1%
6 239
 
1.1%
3 235
 
1.1%
5 232
 
1.0%
9 228
 
1.0%
10 226
 
1.0%
Other values (4) 401
 
1.8%
(Missing) 19435
87.4%
ValueCountFrequency (%)
1 245
1.1%
2 250
1.1%
3 235
1.1%
4 252
1.1%
5 232
1.0%
6 239
1.1%
7 245
1.1%
8 248
1.1%
9 228
1.0%
10 226
1.0%
ValueCountFrequency (%)
14 2
 
< 0.1%
13 5
 
< 0.1%
12 192
0.9%
11 202
0.9%
10 226
1.0%
9 228
1.0%
8 248
1.1%
7 245
1.1%
6 239
1.1%
5 232
1.0%

running_position_6
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)2.8%
Missing21812
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean6.0990566
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:34.866642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2875723
Coefficient of variation (CV)0.53902964
Kurtosis-1.0985686
Mean6.0990566
Median Absolute Deviation (MAD)3
Skewness0.12894774
Sum2586
Variance10.808131
MonotonicityNot monotonic
2025-01-15T18:05:35.040523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 44
 
0.2%
4 42
 
0.2%
3 42
 
0.2%
5 39
 
0.2%
7 38
 
0.2%
1 37
 
0.2%
6 36
 
0.2%
2 35
 
0.2%
10 32
 
0.1%
9 28
 
0.1%
Other values (2) 51
 
0.2%
(Missing) 21812
98.1%
ValueCountFrequency (%)
1 37
0.2%
2 35
0.2%
3 42
0.2%
4 42
0.2%
5 39
0.2%
6 36
0.2%
7 38
0.2%
8 44
0.2%
9 28
0.1%
10 32
0.1%
ValueCountFrequency (%)
12 23
0.1%
11 28
0.1%
10 32
0.1%
9 28
0.1%
8 44
0.2%
7 38
0.2%
6 36
0.2%
5 39
0.2%
4 42
0.2%
3 42
0.2%
Distinct2367
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:35.433695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters177888
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-070
2nd row2016-438
3rd row2015-249
4th row2014-643
5th row2015-252
ValueCountFrequency (%)
2015-607 13
 
0.1%
2016-805 13
 
0.1%
2014-565 12
 
0.1%
2015-640 12
 
0.1%
2014-122 12
 
0.1%
2015-641 12
 
0.1%
2016-049 12
 
0.1%
2016-555 12
 
0.1%
2015-271 12
 
0.1%
2015-767 12
 
0.1%
Other values (2357) 22114
99.5%
2025-01-15T18:05:36.027408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 29574
16.6%
1 29529
16.6%
0 29517
16.6%
- 22236
12.5%
6 14808
8.3%
5 14696
8.3%
4 14584
8.2%
3 7315
 
4.1%
7 6904
 
3.9%
8 4421
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155652
87.5%
Dash Punctuation 22236
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29574
19.0%
1 29529
19.0%
0 29517
19.0%
6 14808
9.5%
5 14696
9.4%
4 14584
9.4%
3 7315
 
4.7%
7 6904
 
4.4%
8 4421
 
2.8%
9 4304
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 22236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177888
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29574
16.6%
1 29529
16.6%
0 29517
16.6%
- 22236
12.5%
6 14808
8.3%
5 14696
8.3%
4 14584
8.2%
3 7315
 
4.1%
7 6904
 
3.9%
8 4421
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29574
16.6%
1 29529
16.6%
0 29517
16.6%
- 22236
12.5%
6 14808
8.3%
5 14696
8.3%
4 14584
8.2%
3 7315
 
4.1%
7 6904
 
3.9%
8 4421
 
2.5%

src
Text

Distinct2367
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:36.312723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length15.079016
Min length15

Characters and Unicode

Total characters335297
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20161001-6.html
2nd row20170222-3.html
3rd row20151213-6.html
4th row20150524-10.html
5th row20151213-9.html
ValueCountFrequency (%)
20160501-7.html 13
 
0.1%
20170716-9.html 13
 
0.1%
20150426-5.html 12
 
0.1%
20160514-3.html 12
 
0.1%
20141029-1.html 12
 
0.1%
20160514-4.html 12
 
0.1%
20160925-3.html 12
 
0.1%
20170409-3.html 12
 
0.1%
20151219-10.html 12
 
0.1%
20160706-3.html 12
 
0.1%
Other values (2357) 22114
99.5%
2025-01-15T18:05:36.774276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 50674
15.1%
1 48772
14.5%
2 37371
11.1%
- 22236
6.6%
. 22236
6.6%
m 22236
6.6%
t 22236
6.6%
h 22236
6.6%
l 22236
6.6%
6 14090
 
4.2%
Other values (6) 50974
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 201881
60.2%
Lowercase Letter 88944
26.5%
Dash Punctuation 22236
 
6.6%
Other Punctuation 22236
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50674
25.1%
1 48772
24.2%
2 37371
18.5%
6 14090
 
7.0%
5 14042
 
7.0%
7 10494
 
5.2%
4 9456
 
4.7%
3 7231
 
3.6%
9 5311
 
2.6%
8 4440
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
m 22236
25.0%
t 22236
25.0%
h 22236
25.0%
l 22236
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 22236
100.0%
Other Punctuation
ValueCountFrequency (%)
. 22236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 246353
73.5%
Latin 88944
 
26.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50674
20.6%
1 48772
19.8%
2 37371
15.2%
- 22236
9.0%
. 22236
9.0%
6 14090
 
5.7%
5 14042
 
5.7%
7 10494
 
4.3%
4 9456
 
3.8%
3 7231
 
2.9%
Other values (2) 9751
 
4.0%
Latin
ValueCountFrequency (%)
m 22236
25.0%
t 22236
25.0%
h 22236
25.0%
l 22236
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50674
15.1%
1 48772
14.5%
2 37371
11.1%
- 22236
6.6%
. 22236
6.6%
m 22236
6.6%
t 22236
6.6%
h 22236
6.6%
l 22236
6.6%
6 14090
 
4.2%
Other values (6) 50974
15.2%
Distinct254
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Minimum2014-09-14 00:00:00
Maximum2017-07-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-15T18:05:37.004342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:37.239632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

race_course
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Sha Tin
14254 
Happy Valley
7982 

Length

Max length12
Median length7
Mean length8.7948372
Min length7

Characters and Unicode

Total characters195562
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSha Tin
2nd rowHappy Valley
3rd rowSha Tin
4th rowSha Tin
5th rowSha Tin

Common Values

ValueCountFrequency (%)
Sha Tin 14254
64.1%
Happy Valley 7982
35.9%

Length

2025-01-15T18:05:37.455087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:37.627242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sha 14254
32.1%
tin 14254
32.1%
happy 7982
17.9%
valley 7982
17.9%

Most occurring characters

ValueCountFrequency (%)
a 30218
15.5%
22236
11.4%
l 15964
8.2%
y 15964
8.2%
p 15964
8.2%
T 14254
7.3%
h 14254
7.3%
S 14254
7.3%
i 14254
7.3%
n 14254
7.3%
Other values (3) 23946
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128854
65.9%
Uppercase Letter 44472
 
22.7%
Space Separator 22236
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30218
23.5%
l 15964
12.4%
y 15964
12.4%
p 15964
12.4%
h 14254
11.1%
i 14254
11.1%
n 14254
11.1%
e 7982
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
T 14254
32.1%
S 14254
32.1%
H 7982
17.9%
V 7982
17.9%
Space Separator
ValueCountFrequency (%)
22236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 173326
88.6%
Common 22236
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30218
17.4%
l 15964
9.2%
y 15964
9.2%
p 15964
9.2%
T 14254
8.2%
h 14254
8.2%
S 14254
8.2%
i 14254
8.2%
n 14254
8.2%
H 7982
 
4.6%
Other values (2) 15964
9.2%
Common
ValueCountFrequency (%)
22236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30218
15.5%
22236
11.4%
l 15964
8.2%
y 15964
8.2%
p 15964
8.2%
T 14254
7.3%
h 14254
7.3%
S 14254
7.3%
i 14254
7.3%
n 14254
7.3%
Other values (3) 23946
12.2%

race_number
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2507196
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:37.788750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7964708
Coefficient of variation (CV)0.53258811
Kurtosis-1.0471074
Mean5.2507196
Median Absolute Deviation (MAD)2
Skewness0.13513503
Sum116755
Variance7.8202491
MonotonicityNot monotonic
2025-01-15T18:05:37.958788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 2422
10.9%
5 2401
10.8%
6 2392
10.8%
2 2388
10.7%
7 2366
10.6%
8 2359
10.6%
3 2347
10.6%
1 2340
10.5%
9 1464
6.6%
10 1384
6.2%
ValueCountFrequency (%)
1 2340
10.5%
2 2388
10.7%
3 2347
10.6%
4 2422
10.9%
5 2401
10.8%
6 2392
10.8%
7 2366
10.6%
8 2359
10.6%
9 1464
6.6%
10 1384
6.2%
ValueCountFrequency (%)
11 373
 
1.7%
10 1384
6.2%
9 1464
6.6%
8 2359
10.6%
7 2366
10.6%
6 2392
10.8%
5 2401
10.8%
4 2422
10.9%
3 2347
10.6%
2 2388
10.7%

race_class
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Class 4
8192 
Class 3
7163 
Class 5
3205 
Class 2
2100 
Class 1
 
404
Other values (11)
1172 

Length

Max length27
Median length7
Mean length7.429079
Min length7

Characters and Unicode

Total characters165193
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClass 3
2nd rowClass 4
3rd rowClass 2
4th rowClass 2
5th rowClass 1

Common Values

ValueCountFrequency (%)
Class 4 8192
36.8%
Class 3 7163
32.2%
Class 5 3205
 
14.4%
Class 2 2100
 
9.4%
Class 1 404
 
1.8%
Group One 278
 
1.3%
Hong Kong Group Three 212
 
1.0%
Griffin Race 119
 
0.5%
Group Two 118
 
0.5%
Group Three 84
 
0.4%
Other values (6) 361
 
1.6%

Length

2025-01-15T18:05:38.164002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
class 21224
46.7%
4 8330
 
18.3%
3 7185
 
15.8%
5 3205
 
7.1%
2 2100
 
4.6%
group 846
 
1.9%
1 404
 
0.9%
hong 366
 
0.8%
kong 366
 
0.8%
one 359
 
0.8%
Other values (7) 1057
 
2.3%

Most occurring characters

ValueCountFrequency (%)
s 42577
25.8%
23206
14.0%
a 21468
13.0%
C 21302
12.9%
l 21302
12.9%
4 8330
 
5.0%
3 7185
 
4.3%
5 3205
 
1.9%
2 2100
 
1.3%
o 1925
 
1.2%
Other values (23) 12593
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96225
58.3%
Uppercase Letter 24218
 
14.7%
Space Separator 23206
 
14.0%
Decimal Number 21224
 
12.8%
Open Punctuation 160
 
0.1%
Close Punctuation 160
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42577
44.2%
a 21468
22.3%
l 21302
22.1%
o 1925
 
2.0%
e 1453
 
1.5%
r 1390
 
1.4%
n 1366
 
1.4%
p 924
 
1.0%
u 846
 
0.9%
g 732
 
0.8%
Other values (7) 2242
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 21302
88.0%
G 965
 
4.0%
T 487
 
2.0%
H 366
 
1.5%
K 366
 
1.5%
O 359
 
1.5%
R 295
 
1.2%
S 78
 
0.3%
Decimal Number
ValueCountFrequency (%)
4 8330
39.2%
3 7185
33.9%
5 3205
 
15.1%
2 2100
 
9.9%
1 404
 
1.9%
Space Separator
ValueCountFrequency (%)
23206
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 120443
72.9%
Common 44750
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42577
35.4%
a 21468
17.8%
C 21302
17.7%
l 21302
17.7%
o 1925
 
1.6%
e 1453
 
1.2%
r 1390
 
1.2%
n 1366
 
1.1%
G 965
 
0.8%
p 924
 
0.8%
Other values (15) 5771
 
4.8%
Common
ValueCountFrequency (%)
23206
51.9%
4 8330
 
18.6%
3 7185
 
16.1%
5 3205
 
7.2%
2 2100
 
4.7%
1 404
 
0.9%
( 160
 
0.4%
) 160
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 42577
25.8%
23206
14.0%
a 21468
13.0%
C 21302
12.9%
l 21302
12.9%
4 8330
 
5.0%
3 7185
 
4.3%
5 3205
 
1.9%
2 2100
 
1.3%
o 1925
 
1.2%
Other values (23) 12593
 
7.6%

race_distance
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1412.138
Minimum1000
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:38.338784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q11200
median1400
Q31650
95-th percentile1800
Maximum2400
Range1400
Interquartile range (IQR)450

Descriptive statistics

Standard deviation281.22487
Coefficient of variation (CV)0.1991483
Kurtosis-0.077981585
Mean1412.138
Median Absolute Deviation (MAD)200
Skewness0.58646508
Sum31400300
Variance79087.428
MonotonicityNot monotonic
2025-01-15T18:05:38.509945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1200 7769
34.9%
1400 3789
17.0%
1650 3658
16.5%
1000 2242
 
10.1%
1600 1977
 
8.9%
1800 1851
 
8.3%
2000 526
 
2.4%
2200 347
 
1.6%
2400 77
 
0.3%
ValueCountFrequency (%)
1000 2242
 
10.1%
1200 7769
34.9%
1400 3789
17.0%
1600 1977
 
8.9%
1650 3658
16.5%
1800 1851
 
8.3%
2000 526
 
2.4%
2200 347
 
1.6%
2400 77
 
0.3%
ValueCountFrequency (%)
2400 77
 
0.3%
2200 347
 
1.6%
2000 526
 
2.4%
1800 1851
 
8.3%
1650 3658
16.5%
1600 1977
 
8.9%
1400 3789
17.0%
1200 7769
34.9%
1000 2242
 
10.1%

track_condition
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
GOOD
12175 
GOOD TO FIRM
8068 
GOOD TO YIELDING
 
1181
YIELDING
 
250
WET SLOW
 
222
Other values (4)
 
340

Length

Max length16
Median length4
Mean length7.6524555
Min length4

Characters and Unicode

Total characters170160
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGOOD
2nd rowGOOD TO FIRM
3rd rowGOOD
4th rowYIELDING
5th rowGOOD

Common Values

ValueCountFrequency (%)
GOOD 12175
54.8%
GOOD TO FIRM 8068
36.3%
GOOD TO YIELDING 1181
 
5.3%
YIELDING 250
 
1.1%
WET SLOW 222
 
1.0%
FAST 191
 
0.9%
WET FAST 132
 
0.6%
SOFT 10
 
< 0.1%
YIELDING TO SOFT 7
 
< 0.1%

Length

2025-01-15T18:05:38.717730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:38.927777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
good 21424
52.1%
to 9256
22.5%
firm 8068
 
19.6%
yielding 1438
 
3.5%
wet 354
 
0.9%
fast 323
 
0.8%
slow 222
 
0.5%
soft 17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 52343
30.8%
G 22862
13.4%
D 22862
13.4%
18866
 
11.1%
I 10944
 
6.4%
T 9950
 
5.8%
F 8408
 
4.9%
R 8068
 
4.7%
M 8068
 
4.7%
E 1792
 
1.1%
Other values (6) 5997
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 151294
88.9%
Space Separator 18866
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 52343
34.6%
G 22862
15.1%
D 22862
15.1%
I 10944
 
7.2%
T 9950
 
6.6%
F 8408
 
5.6%
R 8068
 
5.3%
M 8068
 
5.3%
E 1792
 
1.2%
L 1660
 
1.1%
Other values (5) 4337
 
2.9%
Space Separator
ValueCountFrequency (%)
18866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 151294
88.9%
Common 18866
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 52343
34.6%
G 22862
15.1%
D 22862
15.1%
I 10944
 
7.2%
T 9950
 
6.6%
F 8408
 
5.6%
R 8068
 
5.3%
M 8068
 
5.3%
E 1792
 
1.2%
L 1660
 
1.1%
Other values (5) 4337
 
2.9%
Common
ValueCountFrequency (%)
18866
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 52343
30.8%
G 22862
13.4%
D 22862
13.4%
18866
 
11.1%
I 10944
 
6.4%
T 9950
 
5.8%
F 8408
 
4.9%
R 8068
 
4.7%
M 8068
 
4.7%
E 1792
 
1.1%
Other values (6) 5997
 
3.5%
Distinct1084
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:39.455911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length85
Median length68
Mean length23.271362
Min length12

Characters and Unicode

Total characters517462
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHANGHAI HANDICAP
2nd rowKWAI CHUNG HANDICAP
3rd rowEISHIN PRESTON HANDICAP
4th rowSTAUNTON HANDICAP
5th rowFLYING DANCER HANDICAP
ValueCountFrequency (%)
handicap 21602
28.2%
the 3527
 
4.6%
cup 1994
 
2.6%
kong 893
 
1.2%
hong 780
 
1.0%
club 713
 
0.9%
challenge 646
 
0.8%
trophy 562
 
0.7%
shan 500
 
0.7%
tai 494
 
0.6%
Other values (1186) 45016
58.7%
2025-01-15T18:05:40.251175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 67809
13.1%
54491
10.5%
N 46259
 
8.9%
I 41381
 
8.0%
H 37161
 
7.2%
C 36145
 
7.0%
P 30223
 
5.8%
E 27973
 
5.4%
D 26969
 
5.2%
O 19125
 
3.7%
Other values (41) 129926
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 455648
88.1%
Space Separator 54491
 
10.5%
Open Punctuation 2681
 
0.5%
Close Punctuation 2681
 
0.5%
Other Punctuation 1093
 
0.2%
Decimal Number 525
 
0.1%
Dash Punctuation 279
 
0.1%
Lowercase Letter 64
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 67809
14.9%
N 46259
10.2%
I 41381
 
9.1%
H 37161
 
8.2%
C 36145
 
7.9%
P 30223
 
6.6%
E 27973
 
6.1%
D 26969
 
5.9%
O 19125
 
4.2%
T 17522
 
3.8%
Other values (16) 105081
23.1%
Decimal Number
ValueCountFrequency (%)
1 135
25.7%
4 78
14.9%
0 75
14.3%
3 58
11.0%
2 57
10.9%
5 47
 
9.0%
8 27
 
5.1%
9 21
 
4.0%
7 19
 
3.6%
6 8
 
1.5%
Other Punctuation
ValueCountFrequency (%)
' 645
59.0%
& 227
 
20.8%
. 194
 
17.7%
: 10
 
0.9%
, 9
 
0.8%
@ 8
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
i 29
45.3%
n 14
21.9%
a 7
 
10.9%
o 7
 
10.9%
e 7
 
10.9%
Space Separator
ValueCountFrequency (%)
54491
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2681
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2681
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 455712
88.1%
Common 61750
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 67809
14.9%
N 46259
10.2%
I 41381
 
9.1%
H 37161
 
8.2%
C 36145
 
7.9%
P 30223
 
6.6%
E 27973
 
6.1%
D 26969
 
5.9%
O 19125
 
4.2%
T 17522
 
3.8%
Other values (21) 105145
23.1%
Common
ValueCountFrequency (%)
54491
88.2%
( 2681
 
4.3%
) 2681
 
4.3%
' 645
 
1.0%
- 279
 
0.5%
& 227
 
0.4%
. 194
 
0.3%
1 135
 
0.2%
4 78
 
0.1%
0 75
 
0.1%
Other values (10) 264
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 67809
13.1%
54491
10.5%
N 46259
 
8.9%
I 41381
 
8.0%
H 37161
 
7.2%
C 36145
 
7.0%
P 30223
 
5.8%
E 27973
 
5.4%
D 26969
 
5.2%
O 19125
 
3.7%
Other values (41) 129926
25.1%

track
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
TURF - "A" COURSE
4905 
TURF - "C" COURSE
4170 
TURF - "C+3" COURSE
3780 
TURF - "B" COURSE
2827 
ALL WEATHER TRACK
2723 
Other values (2)
3831 

Length

Max length19
Median length17
Mean length17.684566
Min length17

Characters and Unicode

Total characters393234
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTURF - "A+3" COURSE
2nd rowTURF - "C+3" COURSE
3rd rowTURF - "A" COURSE
4th rowTURF - "C+3" COURSE
5th rowTURF - "A" COURSE

Common Values

ValueCountFrequency (%)
TURF - "A" COURSE 4905
22.1%
TURF - "C" COURSE 4170
18.8%
TURF - "C+3" COURSE 3780
17.0%
TURF - "B" COURSE 2827
12.7%
ALL WEATHER TRACK 2723
12.2%
TURF - "B+2" COURSE 1969
8.9%
TURF - "A+3" COURSE 1862
 
8.4%

Length

2025-01-15T18:05:40.494452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:40.718011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
turf 19513
22.6%
19513
22.6%
course 19513
22.6%
a 4905
 
5.7%
c 4170
 
4.8%
c+3 3780
 
4.4%
b 2827
 
3.3%
all 2723
 
3.2%
weather 2723
 
3.2%
track 2723
 
3.2%
Other values (2) 3831
 
4.4%

Most occurring characters

ValueCountFrequency (%)
63985
16.3%
R 44472
11.3%
" 39026
9.9%
U 39026
9.9%
C 30186
7.7%
T 24959
 
6.3%
E 24959
 
6.3%
F 19513
 
5.0%
O 19513
 
5.0%
- 19513
 
5.0%
Other values (10) 68082
17.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 255488
65.0%
Space Separator 63985
 
16.3%
Other Punctuation 39026
 
9.9%
Dash Punctuation 19513
 
5.0%
Math Symbol 7611
 
1.9%
Decimal Number 7611
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 44472
17.4%
U 39026
15.3%
C 30186
11.8%
T 24959
9.8%
E 24959
9.8%
F 19513
7.6%
O 19513
7.6%
S 19513
7.6%
A 14936
 
5.8%
L 5446
 
2.1%
Other values (4) 12965
 
5.1%
Decimal Number
ValueCountFrequency (%)
3 5642
74.1%
2 1969
 
25.9%
Space Separator
ValueCountFrequency (%)
63985
100.0%
Other Punctuation
ValueCountFrequency (%)
" 39026
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19513
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 255488
65.0%
Common 137746
35.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 44472
17.4%
U 39026
15.3%
C 30186
11.8%
T 24959
9.8%
E 24959
9.8%
F 19513
7.6%
O 19513
7.6%
S 19513
7.6%
A 14936
 
5.8%
L 5446
 
2.1%
Other values (4) 12965
 
5.1%
Common
ValueCountFrequency (%)
63985
46.5%
" 39026
28.3%
- 19513
 
14.2%
+ 7611
 
5.5%
3 5642
 
4.1%
2 1969
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63985
16.3%
R 44472
11.3%
" 39026
9.9%
U 39026
9.9%
C 30186
7.7%
T 24959
 
6.3%
E 24959
 
6.3%
F 19513
 
5.0%
O 19513
 
5.0%
- 19513
 
5.0%
Other values (10) 68082
17.3%
Distinct2367
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:41.228503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length29
Mean length21.168915
Min length17

Characters and Unicode

Total characters470712
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23.70 22.34 22.98
2nd row24.12 23.20 23.05
3rd row24.55 23.86 24.20 22.35
4th row23.84 23.09 24.27
5th row13.56 22.61 24.02 22.06
ValueCountFrequency (%)
23.53 606
 
0.7%
23.70 516
 
0.6%
23.20 485
 
0.6%
23.88 463
 
0.6%
23.92 454
 
0.6%
23.45 448
 
0.5%
23.81 445
 
0.5%
23.62 442
 
0.5%
23.40 440
 
0.5%
23.32 437
 
0.5%
Other values (878) 77422
94.2%
2025-01-15T18:05:41.958149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 106946
22.7%
. 82158
17.5%
59922
12.7%
3 52034
11.1%
4 32883
 
7.0%
1 27866
 
5.9%
5 21546
 
4.6%
7 19139
 
4.1%
8 18337
 
3.9%
6 17047
 
3.6%
Other values (2) 32834
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328632
69.8%
Other Punctuation 82158
 
17.5%
Space Separator 59922
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106946
32.5%
3 52034
15.8%
4 32883
 
10.0%
1 27866
 
8.5%
5 21546
 
6.6%
7 19139
 
5.8%
8 18337
 
5.6%
6 17047
 
5.2%
0 16557
 
5.0%
9 16277
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 82158
100.0%
Space Separator
ValueCountFrequency (%)
59922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 470712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106946
22.7%
. 82158
17.5%
59922
12.7%
3 52034
11.1%
4 32883
 
7.0%
1 27866
 
5.9%
5 21546
 
4.6%
7 19139
 
4.1%
8 18337
 
3.9%
6 17047
 
3.6%
Other values (2) 32834
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 470712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106946
22.7%
. 82158
17.5%
59922
12.7%
3 52034
11.1%
4 32883
 
7.0%
1 27866
 
5.9%
5 21546
 
4.6%
7 19139
 
4.1%
8 18337
 
3.9%
6 17047
 
3.6%
Other values (2) 32834
 
7.0%
Distinct2367
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:42.370248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8275
Median length2939
Mean length2424.0377
Min length92

Characters and Unicode

Total characters53900902
Distinct characters95
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row After beginning awkwardly and making contact with LUCKY GUY, ENDEARING then shifted in, resulting in CAPE THE FAITH which began awkwardly being further hampered. CALL ME AWESOME began awkwardly, shifted out and bumped the hindquarters of CAREFREE LET GO. On the first turn at the 850 Metres, CALL ME AWESOME (K C Leung) shifted out, resulting in ORIONIDS being hampered and taken wider. K C Leung advised that CALL ME AWESOME then hung out throughout the middle stages and he was unable to get the horse to relax whilst racing in the lead. He said as a consequence CALL ME AWESOME was beaten soon after straightening and then weakened in the Straight. A veterinary inspection of CALL ME AWESOME immediately following the race did not show any significant findings. Passing the 500 Metres, LAUGH OUT LOUD was steadied when momentarily tightened for room between LOVELY DELOVELY and GORGEOUS KING which shifted out slightly. CAREFREE LET GO was held up for a short distance in the early part of the Straight. Also in the early part of the Straight, LAUGH OUT LOUD was awkwardly placed outside the heels of GORGEOUS KING. When placed under pressure in the Straight, ORIONIDS raced greenly and was inclined to hang out. When questioned regarding his riding of ROUNDABOUT in the Home Straight, particularly in the early part of the Straight, D Whyte stated that after discussions with connections prior to its last start win, it was felt that ROUNDABOUT gives its best when able to be brought to the outside as the horse is reluctant to improve between runners. He said it was decided to ride ROUNDABOUT in exactly the same manner as its last start by going back from its outside barrier and bringing the horse to the extreme outside on straightening. He said on straightening he brought ROUNDABOUT to the outside and rode the horse in a hands and heels fashion in the early part of the Straight and similar to last start, the horse let down well and commenced to close off strongly. He said that he continued to ride ROUNDABOUT hands and heels before pulling the whip near the 150 Metres as ROUNDABOUT continued to close off well. D Whyte was advised that his explanation would be reported, however, he must ensure that he does not give his mounts too much to do. IRON BOY and LUCKY GUY were sent for sampling.
2nd row KWAICHUNG BROTHERS was slow to begin. GOLDEN ACHIEVER shifted in at the start and bumped AH BO. ROCKET LET WIN began only fairly and then shortly after the start was steadied when crowded for room inside VERY RICH MAN which shifted in. G-ONE LOVER shifted in at the start and bumped GAME OF FUN. From the outside barrier, BRIGHT STAR got its head up when being steadied to be shifted across behind runners in the early stages. CONTRIBUTION lost its right hind plate after the 900 Metres. Approaching and passing the 500 Metres, AH BO got its head on the side and lay out. For the majority of the race, ROCKET LET WIN travelled wide and without cover. The Stewards deferred the declaration of weighed-in as they were of the prima facie view that an incident had occurred approaching the 200 Metres which cast sufficient doubt on whether DR RACE (B Prebble) should be declared the 5th placegetter. When N Callan, the rider of the 6th placegetter, GOLDEN ACHIEVER, did not enter a formal protest/objection on behalf of the connections of GOLDEN ACHIEVER, the Stewards believed that it was appropriate for the matter to proceed to a formal/objection hearing. Whilst these placings did not affect betting, it was relevant that there was the issue of prizemoney in respect of the 5th placegetter. After taking evidence from B Prebble, Mr S K Sit, assistant trainer allocated to Mr D E Ferraris, the trainer of DR RACE, and N Callan, it was found that approaching the 200 Metres DR RACE was shifted to the outside of GAME OF FUN to obtain clear running which resulted in GOLDEN ACHIEVER being checked and losing its rightful running when crowded for room inside CONTRIBUTION. Having regard to the neck margin between both horses at the end of the race and the manner in which they were finishing off the race, the Stewards were satisfied that had the interference not occurred GOLDEN ACHIEVER would have finished in front of DR RACE. Accordingly, the protest/objection was sustained and the placings amended to read No. 3, GAME OF FUN, 1st; No. 11, CONTRIBUTION, 2nd; No. 1, VERY RICH MAN, 3rd; No. 9, G-ONE LOVER, 4th; No. 2, GOLDEN ACHIEVER, 5th; and No. 4, DR RACE, 6th. N Callan was advised that in similar circumstances he must be aware of the placings of horses and to ensure that he has the interests of connections in mind. At a subsequent inquiry, B Prebble pleaded guilty to a charge of careless riding [Rule 100(1)] and was suspended from riding in races for a period to commence on Wednesday, 8 March 2017 and to expire on Monday, 13 March 2017 on which day he may resume race riding (2 Hong Kong racedays). In assessing penalty, the Stewards took into account Jockey Prebble’s good race riding record. G-ONE LOVER, GAME OF FUN and CONTRIBUTION were sent for sampling.
3rd row PHOTON WILLIE was crowded for room on jumping between TRAVEL FIRST and PIKACHU which got its head on the side and shifted in despite the efforts of its rider. APOLLO'S CHOICE, which began awkwardly, and WAH MAY FRIEND bumped at the start. As the start was effected, REGENCY KING lifted its front feet off the ground and then from a wide barrier was shifted across behind runners in the early stages. Also from the outside barrier, WINNING LEADER was taken across behind runners in the early stages. For some distance after the 700 Metres, APOLLO'S CHOICE was awkwardly placed close to the heels of FANTASTIC KAKA. Passing the 350 Metres, WAH MAY FRIEND was awkwardly placed close to the heels of BRILLIANT SHINE after being initially disappointed for running outside that horse. PHOTON WILLIE, which was following, was shifted in away from the heels of WAH MAY FRIEND in consequence. Approaching the 300 Metres, SICHUAN DAR and REGENCY KING made contact as SICHUAN DAR improved into tight running outside WINNING LEADER. Then passing the 300 Metres, SICHUAN DAR was awkwardly placed outside the heels of WINNING LEADER when that horse was taken out by VICTORY MAGIC which was taken wider by ISHVARA. Passing the 300 Metres, PIKACHU was shifted in away from the heels of FANTASTIC KAKA which was giving ground in order to obtain clear running. Throughout the race, BRILLIANT SHINE travelled wide and without cover. The Stewards interviewed M Demuro regarding his riding out of WAH MAY FRIEND over the concluding stages. M Demuro was advised that as the Stewards could not be satisfied to the requisite degree that WAH MAY FRIEND would have finished in front of TRAVEL FIRST, having in mind that he stopped riding his horse over about the final two strides and also having regard to the neck margin between the horses at the end of the race, nonetheless he was severely reprimanded and advised to ensure that he rides his mounts out all the way to the end of the race where circumstances permit. After the race, B Prebble (FANTASTIC KAKA) reported that the horse did not feel comfortable in its action over the latter stages of the race. A veterinary inspection of FANTASTIC KAKA immediately following the race did not show any significant findings. Before being allowed to race again, FANTASTIC KAKA will be subjected to an official veterinary examination. A veterinary inspection of PHOTON WILLIE and BRILLIANT DREAM immediately following the race did not show any significant findings. VICTORY MAGIC, WERTHER and APOLLO'S CHOICE were sent for sampling.
4th row DEEP THINKER was checked when crowded for room on jumping between IMPERIAL CHAMPION and OUR FOLKS which shifted out. CLEVER BEAVER shifted out abruptly at the start, resulting in IMPERIAL ROME being crowded for room out onto DILLY which became unbalanced after being bumped by IMPERIAL ROME. MR GENUINE began awkwardly, shifted out and bumped the hindquarters of GOLDEN DEER, causing both horses to become unbalanced. After this, MR GENUINE and GOLDEN DEER were shifted across behind runners. Near the 1150 Metres, DINING WORLD was hampered and lost ground when crowded for room inside OUR FOLKS (Apprentice H N Wong) which shifted in. Apprentice Wong was severely reprimanded and advised that in similar circumstances he would be expected to make every endeavour to prevent his mounts from shifting ground. Near the 600 Metres, MR GENUINE was steadied away from the heels of OUR FOLKS. Rounding the Home Turn, MR GENUINE raced in restricted room between OUR FOLKS and DEEP THINKER which shifted out. At the entrance to the Straight, DEEP THINKER was shifted to the inside of VICTORY MAGIC to obtain clear running. Passing the 350 Metres, MR GENUINE was steadied and shifted to the outside of VICTORY MAGIC. Passing the 200 Metres, VICTORY MAGIC was shifted out away from the heels of DEEP THINKER which shifted to the outside of ALL GREAT FRIENDS. Near the 150 Metres, J Moreira (VICTORY MAGIC) momentarily lost the use of his whip. After the race, D Whyte stated that DINING WORLD did not stretch out on today’s track at any stage of the race and never travelled comfortably. A veterinary inspection of DINING WORLD immediately following the race did not show any significant findings. A veterinary inspection of DILLY immediately following the race did not show any significant findings. DEEP THINKER and STRATHMORE were sent for sampling.
5th row DIVINE CALLING was withdrawn on 12.12.15 by order of the Stewards acting on veterinary advice (inappetence) and was replaced by Standby Declared Starter KABAYAN (U Rispoli). Before being allowed to race again, DIVINE CALLING will be subjected to an official veterinary examination. KABAYAN was slow to begin. LUCKY BUBBLES began awkwardly. PACKING LLAREGYB began only fairly. SUN JEWELLERY and SUPER LIFELINE bumped at the start. From a wide barrier, I'M IN CHARGE got its head up on a number of occasions when being steadied to be taken across behind runners in the early stages. Making the turn after the 900 Metres, KABAYAN commenced to travel keenly and shifted out away from the heels of I'M IN CHARGE which was awkwardly placed close to the heels of GURUS DREAM. After this, KABAYAN travelled wide and without cover. When questioned regarding his riding of PACKING LLAREGYB in the early part of the Straight, M Demuro stated that his mount was unbalanced after making the Home Turn and was inclined to get its head on the side and lay in. He said he took some time to balance the horse before being able to place it under pressure prior to the 300 Metres. He said after this PACKING LLAREGYB finished off the race fairly. A veterinary inspection of I'M IN CHARGE and EXCITING DREAM immediately following the race did not show any significant findings. SUN JEWELLERY and MULTIVICTORY were sent for sampling.
ValueCountFrequency (%)
the 619027
 
6.9%
to 312548
 
3.5%
and 262140
 
2.9%
was 240761
 
2.7%
of 215421
 
2.4%
in 206606
 
2.3%
metres 121823
 
1.4%
race 111552
 
1.2%
he 106749
 
1.2%
a 106220
 
1.2%
Other values (5699) 6654012
74.3%
2025-01-15T18:05:43.025544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9205192
17.1%
e 4507329
 
8.4%
t 3288106
 
6.1%
a 2834030
 
5.3%
i 2721792
 
5.0%
n 2600180
 
4.8%
r 2313587
 
4.3%
o 2260223
 
4.2%
s 2072826
 
3.8%
h 1874013
 
3.5%
Other values (85) 20223624
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34512671
64.0%
Space Separator 9205605
 
17.1%
Uppercase Letter 8451466
 
15.7%
Other Punctuation 831226
 
1.5%
Decimal Number 490992
 
0.9%
Control 326596
 
0.6%
Open Punctuation 32085
 
0.1%
Close Punctuation 32085
 
0.1%
Dash Punctuation 9039
 
< 0.1%
Math Symbol 5863
 
< 0.1%
Other values (3) 3274
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4507329
13.1%
t 3288106
 
9.5%
a 2834030
 
8.2%
i 2721792
 
7.9%
n 2600180
 
7.5%
r 2313587
 
6.7%
o 2260223
 
6.5%
s 2072826
 
6.0%
h 1874013
 
5.4%
d 1805203
 
5.2%
Other values (18) 8235382
23.9%
Uppercase Letter
ValueCountFrequency (%)
E 803238
 
9.5%
A 752000
 
8.9%
R 627926
 
7.4%
N 587041
 
6.9%
I 575710
 
6.8%
O 565774
 
6.7%
T 501319
 
5.9%
S 484211
 
5.7%
L 429577
 
5.1%
M 387779
 
4.6%
Other values (17) 2736891
32.4%
Decimal Number
ValueCountFrequency (%)
0 251765
51.3%
1 76742
 
15.6%
2 38961
 
7.9%
5 37913
 
7.7%
3 18504
 
3.8%
6 16597
 
3.4%
7 13585
 
2.8%
4 13430
 
2.7%
9 12923
 
2.6%
8 10572
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 425154
51.1%
, 383619
46.2%
' 13441
 
1.6%
/ 6633
 
0.8%
¿ 972
 
0.1%
" 962
 
0.1%
; 212
 
< 0.1%
? 192
 
< 0.1%
: 41
 
< 0.1%
Control
ValueCountFrequency (%)
287247
88.0%
€ 19592
 
6.0%
™ 17761
 
5.4%
 857
 
0.3%
œ 691
 
0.2%
˜ 205
 
0.1%
165
 
0.1%
“ 78
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
> 2912
49.7%
< 2912
49.7%
+ 39
 
0.7%
Space Separator
ValueCountFrequency (%)
9205192
> 99.9%
  413
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 29906
93.2%
[ 2179
 
6.8%
Close Punctuation
ValueCountFrequency (%)
) 29906
93.2%
] 2179
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 9039
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2261
100.0%
Other Number
ValueCountFrequency (%)
½ 993
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42964137
79.7%
Common 10936765
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4507329
 
10.5%
t 3288106
 
7.7%
a 2834030
 
6.6%
i 2721792
 
6.3%
n 2600180
 
6.1%
r 2313587
 
5.4%
o 2260223
 
5.3%
s 2072826
 
4.8%
h 1874013
 
4.4%
d 1805203
 
4.2%
Other values (45) 16686848
38.8%
Common
ValueCountFrequency (%)
9205192
84.2%
. 425154
 
3.9%
, 383619
 
3.5%
287247
 
2.6%
0 251765
 
2.3%
1 76742
 
0.7%
2 38961
 
0.4%
5 37913
 
0.3%
( 29906
 
0.3%
) 29906
 
0.3%
Other values (30) 170360
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53838755
99.9%
None 62147
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9205192
17.1%
e 4507329
 
8.4%
t 3288106
 
6.1%
a 2834030
 
5.3%
i 2721792
 
5.1%
n 2600180
 
4.8%
r 2313587
 
4.3%
o 2260223
 
4.2%
s 2072826
 
3.9%
h 1874013
 
3.5%
Other values (73) 20161477
37.4%
None
ValueCountFrequency (%)
€ 19592
31.5%
â 19592
31.5%
™ 17761
28.6%
½ 993
 
1.6%
ï 972
 
1.6%
¿ 972
 
1.6%
 857
 
1.4%
œ 691
 
1.1%
  413
 
0.7%
˜ 205
 
0.3%
Other values (2) 99
 
0.2%
Distinct17546
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:43.522192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.5105684
Min length1

Characters and Unicode

Total characters211477
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16822 ?
Unique (%)75.7%

Sample

1st row3
2nd row3
3rd row12
4th row7
5th row10
ValueCountFrequency (%)
12 246
 
1.1%
10 205
 
0.9%
8 193
 
0.9%
9 187
 
0.8%
11 183
 
0.8%
1 175
 
0.8%
6 171
 
0.8%
7 169
 
0.8%
5 168
 
0.8%
4 146
 
0.7%
Other values (17536) 20393
91.7%
2025-01-15T18:05:44.245096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 82905
39.2%
1 40697
19.2%
2 16294
 
7.7%
3 9174
 
4.3%
4 9130
 
4.3%
6 9082
 
4.3%
5 9062
 
4.3%
7 9033
 
4.3%
8 8997
 
4.3%
9 8773
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128572
60.8%
Other Punctuation 82905
39.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40697
31.7%
2 16294
12.7%
3 9174
 
7.1%
4 9130
 
7.1%
6 9082
 
7.1%
5 9062
 
7.0%
7 9033
 
7.0%
8 8997
 
7.0%
9 8773
 
6.8%
0 8330
 
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 82905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 211477
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 82905
39.2%
1 40697
19.2%
2 16294
 
7.7%
3 9174
 
4.3%
4 9130
 
4.3%
6 9082
 
4.3%
5 9062
 
4.3%
7 9033
 
4.3%
8 8997
 
4.3%
9 8773
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 82905
39.2%
1 40697
19.2%
2 16294
 
7.7%
3 9174
 
4.3%
4 9130
 
4.3%
6 9082
 
4.3%
5 9062
 
4.3%
7 9033
 
4.3%
8 8997
 
4.3%
9 8773
 
4.1%

recent_ave_rank
Real number (ℝ)

High correlation 

Distinct1264
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4151057
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:44.481031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15.1666667
median6.3076923
Q37.5555556
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)2.3888889

Descriptive statistics

Standard deviation2.0310338
Coefficient of variation (CV)0.31660176
Kurtosis0.45402679
Mean6.4151057
Median Absolute Deviation (MAD)1.1923077
Skewness0.21699097
Sum142646.29
Variance4.1250982
MonotonicityNot monotonic
2025-01-15T18:05:44.716627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 844
 
3.8%
7 756
 
3.4%
5 704
 
3.2%
8 643
 
2.9%
9 480
 
2.2%
4 458
 
2.1%
6.5 396
 
1.8%
10 374
 
1.7%
11 326
 
1.5%
5.5 316
 
1.4%
Other values (1254) 16939
76.2%
ValueCountFrequency (%)
1 218
1.0%
1.25 2
 
< 0.1%
1.333333333 15
 
0.1%
1.375 1
 
< 0.1%
1.4 1
 
< 0.1%
1.428571429 2
 
< 0.1%
1.5 48
 
0.2%
1.6 1
 
< 0.1%
1.666666667 11
 
< 0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
12 298
1.3%
11.75 2
 
< 0.1%
11.66666667 15
 
0.1%
11.6 2
 
< 0.1%
11.5 49
 
0.2%
11.42857143 1
 
< 0.1%
11.4 3
 
< 0.1%
11.33333333 20
 
0.1%
11.28571429 2
 
< 0.1%
11.25 7
 
< 0.1%

Season
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Spring
6671 
Winter
6327 
Autumn
6089 
Summer
3149 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters133416
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutumn
2nd rowWinter
3rd rowWinter
4th rowSpring
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring 6671
30.0%
Winter 6327
28.5%
Autumn 6089
27.4%
Summer 3149
14.2%

Length

2025-01-15T18:05:44.921724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:45.095996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
spring 6671
30.0%
winter 6327
28.5%
autumn 6089
27.4%
summer 3149
14.2%

Most occurring characters

ValueCountFrequency (%)
n 19087
14.3%
r 16147
12.1%
u 15327
11.5%
i 12998
9.7%
t 12416
9.3%
m 12387
9.3%
S 9820
7.4%
e 9476
7.1%
p 6671
 
5.0%
g 6671
 
5.0%
Other values (2) 12416
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111180
83.3%
Uppercase Letter 22236
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 19087
17.2%
r 16147
14.5%
u 15327
13.8%
i 12998
11.7%
t 12416
11.2%
m 12387
11.1%
e 9476
8.5%
p 6671
 
6.0%
g 6671
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
S 9820
44.2%
W 6327
28.5%
A 6089
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 133416
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 19087
14.3%
r 16147
12.1%
u 15327
11.5%
i 12998
9.7%
t 12416
9.3%
m 12387
9.3%
S 9820
7.4%
e 9476
7.1%
p 6671
 
5.0%
g 6671
 
5.0%
Other values (2) 12416
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 19087
14.3%
r 16147
12.1%
u 15327
11.5%
i 12998
9.7%
t 12416
9.3%
m 12387
9.3%
S 9820
7.4%
e 9476
7.1%
p 6671
 
5.0%
g 6671
 
5.0%
Other values (2) 12416
9.3%

jockey_ave_rank
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3829826
Minimum4.0449804
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:45.314752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.0449804
5-th percentile4.0449804
Q15.8194981
median6.3418182
Q37.0281052
95-th percentile8.1186992
Maximum12
Range7.9550196
Interquartile range (IQR)1.2086071

Descriptive statistics

Standard deviation1.0191927
Coefficient of variation (CV)0.15967343
Kurtosis0.67122608
Mean6.3829826
Median Absolute Deviation (MAD)0.61108014
Skewness-0.35076067
Sum141932
Variance1.0387538
MonotonicityNot monotonic
2025-01-15T18:05:45.564925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.044980443 1534
 
6.9%
6.307057057 1332
 
6.0%
4.907332796 1241
 
5.6%
5.730738037 1233
 
5.5%
6.072329689 1189
 
5.3%
7.028105168 1103
 
5.0%
6.745229008 1048
 
4.7%
5.819498069 1036
 
4.7%
6.978346457 1016
 
4.6%
7.363636364 847
 
3.8%
Other values (67) 10657
47.9%
ValueCountFrequency (%)
4.044980443 1534
6.9%
4.25 8
 
< 0.1%
4.5 6
 
< 0.1%
4.907332796 1241
5.6%
5 2
 
< 0.1%
5.053763441 93
 
0.4%
5.166666667 6
 
< 0.1%
5.223214286 112
 
0.5%
5.316455696 79
 
0.4%
5.5 2
 
< 0.1%
ValueCountFrequency (%)
12 3
 
< 0.1%
11.5 2
 
< 0.1%
11 5
 
< 0.1%
10.5 2
 
< 0.1%
10 3
 
< 0.1%
8.833333333 6
 
< 0.1%
8.818428184 369
1.7%
8.75 4
 
< 0.1%
8.5 28
 
0.1%
8.352713178 258
1.2%

trainer_ave_rank
Real number (ℝ)

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3829826
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:45.803564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.0716157
Q16.1608655
median6.3466956
Q36.7722278
95-th percentile7.0607533
Maximum12
Range11
Interquartile range (IQR)0.6113623

Descriptive statistics

Standard deviation0.51818396
Coefficient of variation (CV)0.081182106
Kurtosis10.066139
Mean6.3829826
Median Absolute Deviation (MAD)0.22703092
Skewness0.051703261
Sum141932
Variance0.26851462
MonotonicityNot monotonic
2025-01-15T18:05:46.019840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
6.119664634 1312
 
5.9%
6.356973995 1269
 
5.7%
6.823576584 1247
 
5.6%
6.444805195 1232
 
5.5%
5.861614498 1214
 
5.5%
5.071615721 1145
 
5.1%
6.160865475 1063
 
4.8%
6.639312977 1048
 
4.7%
6.277502478 1009
 
4.5%
6.284860558 1004
 
4.5%
Other values (31) 10693
48.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 10
 
< 0.1%
3.375 8
 
< 0.1%
4 4
 
< 0.1%
5 11
 
< 0.1%
5.071615721 1145
5.1%
5.333333333 3
 
< 0.1%
5.803876853 877
3.9%
5.861614498 1214
5.5%
6 9
 
< 0.1%
ValueCountFrequency (%)
12 8
 
< 0.1%
11 5
 
< 0.1%
10 10
 
< 0.1%
9 2
 
< 0.1%
8.5 4
 
< 0.1%
8 9
 
< 0.1%
7.483495146 515
2.3%
7.228412256 359
1.6%
7.060753341 823
3.7%
7 10
 
< 0.1%

race_course_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
0
14254 
1
7982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22236
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

Length

2025-01-15T18:05:46.217754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:46.376216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

Most occurring characters

ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22236
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

Most occurring scripts

ValueCountFrequency (%)
Common 22236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14254
64.1%
1 7982
35.9%

track_id
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3439468
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:46.518265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9249641
Coefficient of variation (CV)0.57565631
Kurtosis-0.99071673
Mean3.3439468
Median Absolute Deviation (MAD)2
Skewness0.42634415
Sum74356
Variance3.7054866
MonotonicityNot monotonic
2025-01-15T18:05:46.677364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4905
22.1%
2 4170
18.8%
3 3780
17.0%
4 2827
12.7%
5 2723
12.2%
6 1969
8.9%
7 1862
 
8.4%
ValueCountFrequency (%)
1 4905
22.1%
2 4170
18.8%
3 3780
17.0%
4 2827
12.7%
5 2723
12.2%
6 1969
8.9%
7 1862
 
8.4%
ValueCountFrequency (%)
7 1862
 
8.4%
6 1969
8.9%
5 2723
12.2%
4 2827
12.7%
3 3780
17.0%
2 4170
18.8%
1 4905
22.1%

season_id
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
1
6671 
2
6327 
3
6089 
4
3149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22236
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

Length

2025-01-15T18:05:46.862356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T18:05:47.033821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

Most occurring characters

ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22236
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 22236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6671
30.0%
2 6327
28.5%
3 6089
27.4%
4 3149
14.2%

track_condition_id
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6270912
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2025-01-15T18:05:47.203909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95690454
Coefficient of variation (CV)0.5881075
Kurtosis11.155568
Mean1.6270912
Median Absolute Deviation (MAD)0
Skewness2.8025823
Sum36180
Variance0.9156663
MonotonicityNot monotonic
2025-01-15T18:05:47.386778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 12175
54.8%
2 8068
36.3%
3 1181
 
5.3%
4 250
 
1.1%
5 222
 
1.0%
6 191
 
0.9%
7 132
 
0.6%
9 10
 
< 0.1%
8 7
 
< 0.1%
ValueCountFrequency (%)
1 12175
54.8%
2 8068
36.3%
3 1181
 
5.3%
4 250
 
1.1%
5 222
 
1.0%
6 191
 
0.9%
7 132
 
0.6%
8 7
 
< 0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
9 10
 
< 0.1%
8 7
 
< 0.1%
7 132
 
0.6%
6 191
 
0.9%
5 222
 
1.0%
4 250
 
1.1%
3 1181
 
5.3%
2 8068
36.3%
1 12175
54.8%

Interactions

2025-01-15T18:05:18.859739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:03:59.781464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:03.889508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:08.194744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:11.961802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:20.058559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:24.056754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:27.805967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:31.546245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:35.556413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:01.433122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:05.386781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:09.675811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:13.401027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:17.589389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:21.532719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:05:08.288619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:18.430889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:22.358280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:26.350964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:30.098114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:34.122449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:41.916775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:49.766795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:53.380958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:57.454080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:01.342595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:05.234799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:09.130402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:14.600721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:18.604731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:22.760074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:34.280690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:05:17.560728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:02.608682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:06.534606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:10.799695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:14.757406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:18.771992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:22.906345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:26.656553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:30.400808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:34.426559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:43.413092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:05:09.457665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:13.371949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:07.188257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:10.950640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:14.906713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:18.940852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.056483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:26.805402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:30.548689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:34.571826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:43.977984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:50.217981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:53.823215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:57.915969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:01.844220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:05.687219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:09.619687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:13.522537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:17.853917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:21.915845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:02.970863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:07.358656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:11.119379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:15.075889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:19.130088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.225994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:26.975705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:30.721699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:34.727921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:44.560129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:50.380203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:53.983620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:58.082778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:02.038093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:05.856697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:09.803355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:13.696436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:18.025908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:22.083279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:03.147370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:07.515331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:11.276978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:15.232571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:19.311165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.384330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:27.133028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:30.879170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:34.884103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:45.131693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:50.537110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:54.124380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:58.237693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:02.204721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:06.014197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:09.974867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:05:18.184798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:11.453895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:15.415371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:19.508865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.559682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:27.306601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:31.055625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:35.059642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:45.717863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:50.709595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:54.270866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:58.413857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:02.402738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:06.190132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:10.162213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:14.036846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:18.362057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:22.433959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:03.515343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:07.853276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:11.614867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:15.572105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:19.684453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.715395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:27.461989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:31.208982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:35.206214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:50.858313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-15T18:04:58.567417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:02.569674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:06.344264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:10.332514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:14.197835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:18.517730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:22.601898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:03.698677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:08.014514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:11.776390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:15.729672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:19.865469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:23.873880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:27.622454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:31.367840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:35.366268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:46.867116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:51.017791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:54.567449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:04:58.724862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:02.737000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:06.504906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:10.505901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:14.361418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-15T18:05:18.678509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-15T18:05:47.580321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Seasonactual_weightdeclared_horse_weightdrawfinishing_positionhorse_numberjockey_ave_rankrace_classrace_courserace_course_idrace_distancerace_numberrecent_ave_rankrunning_position_1running_position_2running_position_3running_position_4running_position_5running_position_6season_idtracktrack_conditiontrack_condition_idtrack_idtrainer_ave_rankwin_odds
Season1.0000.0430.0390.0000.0000.0000.2320.1320.0230.0230.0570.0180.0150.0000.0000.0000.0000.0000.0001.0000.0740.1660.1660.0740.0450.033
actual_weight0.0431.0000.0350.003-0.107-0.825-0.3740.1200.0400.0400.015-0.084-0.080-0.011-0.017-0.055-0.089-0.043-0.1010.0430.0280.008-0.010-0.010-0.091-0.232
declared_horse_weight0.0390.0351.000-0.004-0.056-0.049-0.0640.0580.0760.076-0.1180.102-0.108-0.125-0.128-0.116-0.049-0.079-0.0010.0390.0180.000-0.0140.025-0.095-0.092
draw0.0000.003-0.0041.0000.1120.022-0.0010.0000.1860.1860.0000.0250.0380.2250.2040.1550.1220.1040.1170.0000.0430.0000.0050.0130.0020.189
finishing_position0.000-0.107-0.0560.1121.0000.1090.2820.0000.0000.000-0.0050.0090.5710.2240.2760.5730.8160.8901.0000.0000.0000.000-0.008-0.0030.1350.498
horse_number0.000-0.825-0.0490.0220.1091.0000.2610.0010.1900.1900.0090.0130.1000.0780.0810.0920.0960.0750.0900.0000.0420.0000.0040.0200.1050.214
jockey_ave_rank0.232-0.374-0.064-0.0010.2820.2611.0000.1140.1000.100-0.047-0.0360.2550.0810.0970.1690.2280.2240.2810.2320.0770.0690.0130.0190.2300.536
race_class0.1320.1200.0580.0000.0000.0010.1141.0000.1940.1940.1980.3380.0790.0000.0000.0000.0000.0000.0000.1320.1270.0520.0520.1270.1770.063
race_course0.0230.0400.0760.1860.0000.1900.1000.1941.0001.0000.3850.2550.0620.1810.1740.1420.0660.0000.0000.0230.5370.1610.1610.5370.1300.093
race_course_id0.0230.0400.0760.1860.0000.1900.1000.1941.0001.0000.3850.2550.0620.1810.1740.1420.0660.0000.0000.0230.5370.1610.1610.5370.1300.093
race_distance0.0570.015-0.1180.000-0.0050.009-0.0470.1980.3850.3851.0000.0760.0200.0160.0200.0340.012-0.013-0.0660.0570.1600.0830.008-0.013-0.026-0.043
race_number0.018-0.0840.1020.0250.0090.013-0.0360.3380.2550.2550.0761.000-0.1420.0220.0220.0220.005-0.0040.0150.0180.0700.0500.0560.019-0.1940.035
recent_ave_rank0.015-0.080-0.1080.0380.5710.1000.2550.0790.0620.0620.020-0.1421.0000.1820.2120.3560.4510.4510.5050.0150.0200.009-0.0050.0070.2320.443
running_position_10.000-0.011-0.1250.2250.2240.0780.0810.0000.1810.1810.0160.0220.1821.0000.9330.6310.3040.171-0.0560.0000.0360.0000.0040.0150.0650.284
running_position_20.000-0.017-0.1280.2040.2760.0810.0970.0000.1740.1740.0200.0220.2120.9331.0000.7140.3330.177-0.0660.0000.0380.0000.0020.0120.0660.302
running_position_30.000-0.055-0.1160.1550.5730.0920.1690.0000.1420.1420.0340.0220.3560.6310.7141.0000.4300.203-0.0790.0000.0230.000-0.0020.0020.1020.360
running_position_40.000-0.089-0.0490.1220.8160.0960.2280.0000.0660.0660.0120.0050.4510.3040.3330.4301.0000.340-0.0220.0000.0000.000-0.0080.0010.1140.418
running_position_50.000-0.043-0.0790.1040.8900.0750.2240.0000.0000.000-0.013-0.0040.4510.1710.1770.2030.3401.0000.2460.0000.0000.000-0.008-0.0020.1220.398
running_position_60.000-0.101-0.0010.1171.0000.0900.2810.0000.0000.000-0.0660.0150.505-0.056-0.066-0.079-0.0220.2461.0000.0000.0000.000-0.0380.0510.1590.511
season_id1.0000.0430.0390.0000.0000.0000.2320.1320.0230.0230.0570.0180.0150.0000.0000.0000.0000.0000.0001.0000.0740.1660.1660.0740.0450.033
track0.0740.0280.0180.0430.0000.0420.0770.1270.5370.5370.1600.0700.0200.0360.0380.0230.0000.0000.0000.0741.0000.2270.2271.0000.0650.031
track_condition0.1660.0080.0000.0000.0000.0000.0690.0520.1610.1610.0830.0500.0090.0000.0000.0000.0000.0000.0000.1660.2271.0001.0000.2270.0220.032
track_condition_id0.166-0.010-0.0140.005-0.0080.0040.0130.0520.1610.1610.0080.056-0.0050.0040.002-0.002-0.008-0.008-0.0380.1660.2271.0001.000-0.071-0.019-0.009
track_id0.074-0.0100.0250.013-0.0030.0200.0190.1270.5370.537-0.0130.0190.0070.0150.0120.0020.001-0.0020.0510.0741.0000.227-0.0711.0000.0140.004
trainer_ave_rank0.045-0.091-0.0950.0020.1350.1050.2300.1770.1300.130-0.026-0.1940.2320.0650.0660.1020.1140.1220.1590.0450.0650.022-0.0190.0141.0000.190
win_odds0.033-0.232-0.0920.1890.4980.2140.5360.0630.0930.093-0.0430.0350.4430.2840.3020.3600.4180.3980.5110.0330.0310.032-0.0090.0040.1901.000

Missing values

2025-01-15T18:05:22.966516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-15T18:05:23.869551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-15T18:05:24.386128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

finishing_positionhorse_numberhorse_namehorse_idjockeytraineractual_weightdeclared_horse_weightdrawlength_behind_winnerrunning_position_1running_position_2running_position_3running_position_4finish_timewin_oddsrunning_position_5running_position_6race_idsrcrace_daterace_courserace_numberrace_classrace_distancetrack_conditionrace_nametracksectional_timeincident_reportrecent_6_runsrecent_ave_rankSeasonjockey_ave_ranktrainer_ave_rankrace_course_idtrack_idseason_idtrack_condition_id
0312.0CAREFREE LET GOT059M L YeungC S Shum1121060226.04.03.0NaN1.09.339.2NaNNaN2016-07020161001-6.html2016-10-01Sha Tin6Class 31200GOODSHANGHAI HANDICAPTURF - "A+3" COURSE23.70 22.34 22.98\n After beginning awkwardly and making contact with LUCKY GUY, ENDEARING then shifted in, resulting in CAPE THE FAITH which began awkwardly being further hampered.\nCALL ME AWESOME began awkwardly, shifted out and bumped the hindquarters of CAREFREE LET GO.\nOn the first turn at the 850 Metres, CALL ME AWESOME (K C Leung) shifted out, resulting in ORIONIDS being hampered and taken wider. K C Leung advised that CALL ME AWESOME then hung out throughout the middle stages and he was unable to get the horse to relax whilst racing in the lead. He said as a consequence CALL ME AWESOME was beaten soon after straightening and then weakened in the Straight. A veterinary inspection of CALL ME AWESOME immediately following the race did not show any significant findings.\nPassing the 500 Metres, LAUGH OUT LOUD was steadied when momentarily tightened for room between LOVELY DELOVELY and GORGEOUS KING which shifted out slightly.\nCAREFREE LET GO was held up for a short distance in the early part of the Straight.\nAlso in the early part of the Straight, LAUGH OUT LOUD was awkwardly placed outside the heels of GORGEOUS KING.\nWhen placed under pressure in the Straight, ORIONIDS raced greenly and was inclined to hang out.\nWhen questioned regarding his riding of ROUNDABOUT in the Home Straight, particularly in the early part of the Straight, D Whyte stated that after discussions with connections prior to its last start win, it was felt that ROUNDABOUT gives its best when able to be brought to the outside as the horse is reluctant to improve between runners. He said it was decided to ride ROUNDABOUT in exactly the same manner as its last start by going back from its outside barrier and bringing the horse to the extreme outside on straightening. He said on straightening he brought ROUNDABOUT to the outside and rode the horse in a hands and heels fashion in the early part of the Straight and similar to last start, the horse let down well and commenced to close off strongly. He said that he continued to ride ROUNDABOUT hands and heels before pulling the whip near the 150 Metres as ROUNDABOUT continued to close off well. D Whyte was advised that his explanation would be reported, however, he must ensure that he does not give his mounts too much to do.\nIRON BOY and LUCKY GUY were sent for sampling.\n33.0Autumn6.9783466.1608650731
131.0VERY RICH MANV286U RispoliT K Ng133105793/41.01.03.0NaN1.10.5319NaNNaN2016-43820170222-3.html2017-02-22Happy Valley3Class 41200GOOD TO FIRMKWAI CHUNG HANDICAPTURF - "C+3" COURSE24.12 23.20 23.05\n KWAICHUNG BROTHERS was slow to begin.\nGOLDEN ACHIEVER shifted in at the start and bumped AH BO.\nROCKET LET WIN began only fairly and then shortly after the start was steadied when crowded for room inside VERY RICH MAN which shifted in.\nG-ONE LOVER shifted in at the start and bumped GAME OF FUN.\nFrom the outside barrier, BRIGHT STAR got its head up when being steadied to be shifted across behind runners in the early stages.\nCONTRIBUTION lost its right hind plate after the 900 Metres.\nApproaching and passing the 500 Metres, AH BO got its head on the side and lay out.\nFor the majority of the race, ROCKET LET WIN travelled wide and without cover.\nThe Stewards deferred the declaration of weighed-in as they were of the prima facie view that an incident had occurred approaching the 200 Metres which cast sufficient doubt on whether DR RACE (B Prebble) should be declared the 5th placegetter. When N Callan, the rider of the 6th placegetter, GOLDEN ACHIEVER, did not enter a formal protest/objection on behalf of the connections of GOLDEN ACHIEVER, the Stewards believed that it was appropriate for the matter to proceed to a formal/objection hearing. Whilst these placings did not affect betting, it was relevant that there was the issue of prizemoney in respect of the 5th placegetter. After taking evidence from B Prebble, Mr S K Sit, assistant trainer allocated to Mr D E Ferraris, the trainer of DR RACE, and N Callan, it was found that approaching the 200 Metres DR RACE was shifted to the outside of GAME OF FUN to obtain clear running which resulted in GOLDEN ACHIEVER being checked and losing its rightful running when crowded for room inside CONTRIBUTION. Having regard to the neck margin between both horses at the end of the race and the manner in which they were finishing off the race, the Stewards were satisfied that had the interference not occurred GOLDEN ACHIEVER would have finished in front of DR RACE. Accordingly, the protest/objection was sustained and the placings amended to read No. 3, GAME OF FUN, 1st; No. 11, CONTRIBUTION, 2nd; No. 1, VERY RICH MAN, 3rd; No. 9, G-ONE LOVER, 4th; No. 2, GOLDEN ACHIEVER, 5th; and No. 4, DR RACE, 6th. N Callan was advised that in similar circumstances he must be aware of the placings of horses and to ensure that he has the interests of connections in mind. At a subsequent inquiry, B Prebble pleaded guilty to a charge of careless riding [Rule 100(1)] and was suspended from riding in races for a period to commence on Wednesday, 8 March 2017 and to expire on Monday, 13 March 2017 on which day he may resume race riding (2 Hong Kong racedays). In assessing penalty, the Stewards took into account Jockey Prebble’s good race riding record.\nG-ONE LOVER, GAME OF FUN and CONTRIBUTION were sent for sampling.\n33.0Winter6.0460367.4834951322
2124.0FANTASTIC KAKAP363B PrebbleL Ho125112517-1/43.04.05.012.01.36.1319NaNNaN2015-24920151213-6.html2015-12-13Sha Tin6Class 21600GOODEISHIN PRESTON HANDICAPTURF - "A" COURSE24.55 23.86 24.20 22.35\n PHOTON WILLIE was crowded for room on jumping between TRAVEL FIRST and PIKACHU which got its head on the side and shifted in despite the efforts of its rider.\nAPOLLO'S CHOICE, which began awkwardly, and WAH MAY FRIEND bumped at the start.\nAs the start was effected, REGENCY KING lifted its front feet off the ground and then from a wide barrier was shifted across behind runners in the early stages.\nAlso from the outside barrier, WINNING LEADER was taken across behind runners in the early stages.\nFor some distance after the 700 Metres, APOLLO'S CHOICE was awkwardly placed close to the heels of FANTASTIC KAKA.\nPassing the 350 Metres, WAH MAY FRIEND was awkwardly placed close to the heels of BRILLIANT SHINE after being initially disappointed for running outside that horse. PHOTON WILLIE, which was following, was shifted in away from the heels of WAH MAY FRIEND in consequence.\nApproaching the 300 Metres, SICHUAN DAR and REGENCY KING made contact as SICHUAN DAR improved into tight running outside WINNING LEADER. Then passing the 300 Metres, SICHUAN DAR was awkwardly placed outside the heels of WINNING LEADER when that horse was taken out by VICTORY MAGIC which was taken wider by ISHVARA.\nPassing the 300 Metres, PIKACHU was shifted in away from the heels of FANTASTIC KAKA which was giving ground in order to obtain clear running.\nThroughout the race, BRILLIANT SHINE travelled wide and without cover.\nThe Stewards interviewed M Demuro regarding his riding out of WAH MAY FRIEND over the concluding stages. M Demuro was advised that as the Stewards could not be satisfied to the requisite degree that WAH MAY FRIEND would have finished in front of TRAVEL FIRST, having in mind that he stopped riding his horse over about the final two strides and also having regard to the neck margin between the horses at the end of the race, nonetheless he was severely reprimanded and advised to ensure that he rides his mounts out all the way to the end of the race where circumstances permit. \nAfter the race, B Prebble (FANTASTIC KAKA) reported that the horse did not feel comfortable in its action over the latter stages of the race. A veterinary inspection of FANTASTIC KAKA immediately following the race did not show any significant findings. Before being allowed to race again, FANTASTIC KAKA will be subjected to an official veterinary examination.\nA veterinary inspection of PHOTON WILLIE and BRILLIANT DREAM immediately following the race did not show any significant findings.\nVICTORY MAGIC, WERTHER and APOLLO'S CHOICE were sent for sampling.\n1212.0Winter5.8194986.6284950121
376.0VICTORY MAGICT272J MoreiraJ Moore122115413-1/26.04.07.0NaN1.11.7713NaNNaN2014-64320150524-10.html2015-05-24Sha Tin10Class 21200YIELDINGSTAUNTON HANDICAPTURF - "C+3" COURSE23.84 23.09 24.27\n DEEP THINKER was checked when crowded for room on jumping between IMPERIAL CHAMPION and OUR FOLKS which shifted out.\nCLEVER BEAVER shifted out abruptly at the start, resulting in IMPERIAL ROME being crowded for room out onto DILLY which became unbalanced after being bumped by IMPERIAL ROME.\nMR GENUINE began awkwardly, shifted out and bumped the hindquarters of GOLDEN DEER, causing both horses to become unbalanced. After this, MR GENUINE and GOLDEN DEER were shifted across behind runners.\nNear the 1150 Metres, DINING WORLD was hampered and lost ground when crowded for room inside OUR FOLKS (Apprentice H N Wong) which shifted in. Apprentice Wong was severely reprimanded and advised that in similar circumstances he would be expected to make every endeavour to prevent his mounts from shifting ground.\nNear the 600 Metres, MR GENUINE was steadied away from the heels of OUR FOLKS.\nRounding the Home Turn, MR GENUINE raced in restricted room between OUR FOLKS and DEEP THINKER which shifted out.\nAt the entrance to the Straight, DEEP THINKER was shifted to the inside of VICTORY MAGIC to obtain clear running.\nPassing the 350 Metres, MR GENUINE was steadied and shifted to the outside of VICTORY MAGIC.\nPassing the 200 Metres, VICTORY MAGIC was shifted out away from the heels of DEEP THINKER which shifted to the outside of ALL GREAT FRIENDS.\nNear the 150 Metres, J Moreira (VICTORY MAGIC) momentarily lost the use of his whip.\nAfter the race, D Whyte stated that DINING WORLD did not stretch out on today’s track at any stage of the race and never travelled comfortably. A veterinary inspection of DINING WORLD immediately following the race did not show any significant findings.\nA veterinary inspection of DILLY immediately following the race did not show any significant findings.\nDEEP THINKER and STRATHMORE were sent for sampling.\n77.0Spring4.044985.8616140314
4104.0EXCITING DREAMP191H BowmanJ Moore127107914-3/45.05.05.010.01.23.0214NaNNaN2015-25220151213-9.html2015-12-13Sha Tin9Class 11400GOODFLYING DANCER HANDICAPTURF - "A" COURSE13.56 22.61 24.02 22.06\n DIVINE CALLING was withdrawn on 12.12.15 by order of the Stewards acting on veterinary advice (inappetence) and was replaced by Standby Declared Starter KABAYAN (U Rispoli). Before being allowed to race again, DIVINE CALLING will be subjected to an official veterinary examination.\nKABAYAN was slow to begin.\nLUCKY BUBBLES began awkwardly.\nPACKING LLAREGYB began only fairly.\nSUN JEWELLERY and SUPER LIFELINE bumped at the start.\nFrom a wide barrier, I'M IN CHARGE got its head up on a number of occasions when being steadied to be taken across behind runners in the early stages.\nMaking the turn after the 900 Metres, KABAYAN commenced to travel keenly and shifted out away from the heels of I'M IN CHARGE which was awkwardly placed close to the heels of GURUS DREAM. After this, KABAYAN travelled wide and without cover.\nWhen questioned regarding his riding of PACKING LLAREGYB in the early part of the Straight, M Demuro stated that his mount was unbalanced after making the Home Turn and was inclined to get its head on the side and lay in. He said he took some time to balance the horse before being able to place it under pressure prior to the 300 Metres. He said after this PACKING LLAREGYB finished off the race fairly.\nA veterinary inspection of I'M IN CHARGE and EXCITING DREAM immediately following the race did not show any significant findings.\nSUN JEWELLERY and MULTIVICTORY were sent for sampling.\n1010.0Winter5.3164565.8616140121
593.0WINNAMP119G MosseT P Yung132118166-3/412.012.012.09.01.35.5921NaNNaN2015-30020160101-10.html2016-01-01Sha Tin10Class 31600GOODKOWLOON PEAK HANDICAPTURF - "B+2" COURSE24.09 22.14 24.40 23.88\n APPROVE began awkwardly.\nTRAVEL AMBASSADOR was slow to begin.\nJOLLY JOLLY began only fairly and was ridden along in the early stages to make up lost ground.\nFrom wide barriers, GOOD CHOICE and VICTORY MASTER were shifted across behind runners in the early stages.\nAt the 1500 Metres, EASTERN EXPRESS and HAPPY AGILITY bumped.\nNear the 1300 Metres, HAPPY AGILITY was awkwardly placed close to the heels of GRAND HARBOUR.\nAgain making the turn after the 900 Metres, HAPPY AGILITY was steadied away from the heels of GRAND HARBOUR which got its head up when racing close to the heels of APPROVE. After this, HAPPY AGILITY continued to prove very difficult to settle and shifted out away from the heels of GRAND HARBOUR, resulting in EASTERN EXPRESS being taken wider.\nPassing the 800 Metres, EASTERN EXPRESS was steadied to obtain a position with cover behind HAPPY AGILITY.\nKIRAM had difficulty obtaining clear running until after the 300 Metres.\nPassing the 300 Metres, GOOD CHOICE raced in restricted room between TRAVEL AMBASSADOR and GRAND HARBOUR which lay in under pressure. GOOD CHOICE then had difficulty obtaining clear running approaching the 200 Metres when improving into tight running between CHEERFUL BOY and GRAND HARBOUR.\nApproaching the 100 Metres, GRAND HARBOUR was momentarily steadied away from the heels of THE SHOW.\nThroughout the race, THE SHOW travelled wide and without cover.\nThe performance of APPROVE, which gave ground abruptly in the Straight and subsequently finished tailed out, was considered unacceptable. Before being allowed to race again, APPROVE will be required to perform to the satisfaction of the Stewards in a barrier trial and be subjected to an official veterinary examination.\nA veterinary inspection of APPROVE and JOLLY JOLLY immediately following the race did not show any significant findings.\nJOLLY JOLLY, GONNA RUN and KIRAM were sent for sampling.\n99.0Winter6.0951426.3466960621
632.0GREAT SKYN426N CallanA T Millard13111841027.06.03.0NaN1.09.669.4NaNNaN2014-03120140924-3.html2014-09-24Sha Tin3Class 31200GOODLEK YUEN HANDICAPALL WEATHER TRACK23.77 22.86 22.71\n As the start was effected, SIGHT BELIEVER lifted its front feet off the ground and consequently began only fairly. Then a short distance after the start, SIGHT BELIEVER was steadied away from the heels of CHEETAH BOY which shifted out.\nWINNING INSTINCT shifted out at the start, resulting in CAGA FORCE being hampered.\nALL WIN BOY was tardy to begin.\nAfter the 600 Metres, SIGHT BELIEVER was awkwardly placed close to the heels of EN CIVIL.\nRounding the Home Turn, GREAT SKY and CHEETAH BOY raced tight due to CHEETAH BOY shifting out to obtain clear running.\nIn the Straight, CHEETAH BOY got its head on the side and lay out under pressure, resulting in GREAT SKY being inconvenienced in the early part of the Straight and again near the 100 Metres.\nThroughout the race, RUMBA KING travelled wide and without cover.\nAfter the race, D Whyte reported that he was unable to offer any explanation for the disappointing performance of CAGA FORCE. He said the horse travelled satisfactorily in the early and middle stages, however, after coming under pressure near the 400 Metres was most disappointing in the manner in which it weakened out of the race in the Straight. Mr C S Shum, the trainer of CAGA FORCE, was unable to offer any explanation for the horse's disappointing performance other than it may not have been suited by the addition of blinkers. He said he had studied the videos of CAGA FORCE, which was having its first start for his stable tonight, and he was of the belief that the horse was not finishing off its races. He said it was for this reason that he applied blinkers to the horse. He said CAGA FORCE's track work in this piece of gear had indicated that it would benefit from wearing it, however, in tonight's race, he felt the horse was inclined to race too keenly with the blinkers and therefore he would consider altering the horse's gear in its future starts. A veterinary inspection of CAGA FORCE immediately following the race did not show any significant findings. The performance of CAGA FORCE was considered unacceptable. Before being allowed to race again, CAGA FORCE will be required to perform to the satisfaction of the Stewards in an official barrier trial and be subjected to an official veterinary examination.\nA veterinary inspection of SIGHT BELIEVER immediately following the race did not show any significant findings.\nRUMBA KING and WINNING INSTINCT were sent for sampling.\n33.0Autumn6.072336.2848610531
793.0COUNTRY MELODYT011B PrebbleJ Size1311104435.06.09.0NaN0.56.6842NaNNaN2016-11120161016-10.html2016-10-16Sha Tin10Class 21000GOOD TO FIRMTAILORBIRD HANDICAPTURF - "C" COURSE12.98 20.81 22.41\n Just prior to the start being effected, ARCHIPPUS became fractious and then was slow to begin.\nShortly after the start, RACING SUPERNOVA was crowded for room between HAPPY YEAH YEAH and MY LITTLE FRIEND which shifted out. The tightening to RACING SUPERNOVA was exacerbated when HAPPY YEAH YEAH shifted in when bumped on the hindquarters by RACING SUPERNOVA. This resulted in RACING SUPERNOVA losing ground and racing towards the rear of the field.\nApproaching the 800 Metres, RACING SUPERNOVA momentarily raced in restricted room between HAPPY YEAH YEAH and MY LITTLE FRIEND which got its head on the side and shifted out despite the efforts of its rider.\nPassing the 400 Metres, RACING SUPERNOVA was shifted to the outside of MALMSTEEN after being disappointed for running between that horse and COUNTRY MELODY.\nARCHIPPUS was unable to obtain clear running until over the final 300 Metres and consequently was not able to be tested.\nPassing the 200 Metres, SMART DECLARATION was crowded for room between GOLDEN HARVEST and TRIUMPHANT JEWEL which shifted out marginally.\nPassing the 100 Metres, RACING SUPERNOVA raced tight between CHARITY JOY and HELLA HEDGE which was racing tight outside BAD BOY.\nOver the concluding stages, RACING SUPERNOVA raced close to the heels of ADVENTURER.\nRACING SUPERNOVA, ADVENTURER and BAD BOY were sent for sampling.\n99.0Autumn5.8194985.0716160232
8814.0PERFECT TIMINGT019M ChadwickT P Yung1131112113-1/214.014.011.08.01.36.3159NaNNaN2016-46320170305-2.html2017-03-05Sha Tin2Class 51600GOOD TO FIRMLOTUS BRIDGE HANDICAPTURF - "C" COURSE24.31 22.64 24.68 24.13\n STORM KID began awkwardly and lost ground.\nPOLYMER LUCK shifted out at the start and bumped SPICY DOUBLE.\nHAR HAR CHARMING began only fairly.\nAfter travelling a short distance, RED HORSE shifted out and bumped the hindquarters of AUDACITY which became unbalanced.\nApproaching the 1500 Metres, MY FOLKS was awkwardly placed inside the heels of STRIKING STAR.\nApproaching and passing the 1400 Metres, STRIKING STAR got its head up when travelling keenly.\nU Rispoli (HAR HAR CHARMING) pleaded guilty to a charge of careless riding [Rule 100(1)] in that approaching the 1200 Metres he permitted his mount to shift in when not clear of FORTUNE GIGGLES, resulting in that horse being taken in onto STRIKING STAR which in turn was taken in onto MY FOLKS, causing that horse to be crowded for room and checked. U Rispoli was suspended from riding in races for a period to commence on Sunday, 12 March 2017 and to expire on Thursday, 16 March 2017 on which day he may resume race riding (2 Hong Kong racedays). In assessing penalty, the Stewards took into account Jockey Rispoli's good race riding record.\nAfter the 1100 Metres, POLYMER LUCK proved difficult to settle and got its head up for some distance when racing without cover.\nPassing the 900 Metres, AUDACITY was crowded for room between POLYMER LUCK and DASHING DART which shifted out away from the heels of MY FOLKS.\nPassing the 600 Metres, POLYMER LUCK was checked when proving difficult to settle and awkwardly placed behind RED HORSE.\nAlso passing the 600 Metres, SPICY DOUBLE proved difficult to settle and was checked out away from the heels of DASHING DART.\nNear the 300 Metres, DASHING DART was shifted to the outside of RED HORSE after being disappointed for running between that horse and EMPIRE OF MONGOLIA. When being shifted out further to obtain clear running, DASHING DART was awkwardly placed close to the heels of POLYMER LUCK a short distance later.\nApproaching the 200 Metres, SPICY DOUBLE was crowded for room inside AUDACITY by PERFECT TIMING (M Chadwick) which shifted out to obtain clear running. M Chadwick was advised to exercise more care.\nNear the 50 Metres, RED HORSE lay in under pressure. \nThroughout the race, HAR HAR CHARMING travelled wide and without cover and in the Straight gave ground. A veterinary inspection of HAR HAR CHARMING immediately following the race did not show any significant findings. The performance of HAR HAR CHARMING, which finished tailed out, was considered unacceptable. Before being allowed to race again, HAR HAR CHARMING will be required to perform to the satisfaction of the Stewards in a barrier trial and be subjected to an official veterinary examination.\nDASHING DART and POLYMER LUCK were sent for sampling.\n88.0Spring6.9067226.3466960212
9102.0TWIN DELIGHTS125Z PurtonC Fownes13310881454.02.02.010.01.37.9237NaNNaN2015-18720151118-8.html2015-11-18Sha Tin8Class 21650GOODSUTHERLAND HANDICAPALL WEATHER TRACK27.46 22.31 23.71 23.66\n From a wide barrier, GOLDLAND DANCER was shifted across behind runners in the early stages.\nHIGH AND MIGHTY lost its left front plate in the early stages.\nApproaching the 1200 Metres, MAJESTIC ANTHEM momentarily raced tight inside WINNIE'S HORSE which got its head on the side and shifted in away from OBLITERATOR.\nApproaching the 1000 Metres, MAJESTIC ANTHEM was again crowded for room inside WINNIE'S HORSE which was racing tight inside OBLITERATOR (N Callan). N Callan was advised to ensure that he leaves sufficient racing room for runners to his inside in future.\nAfter the 900 Metres, GOAL FOR GOLD was left racing wide and without cover.\nGOODHEART SUCCESS lost its left front plate whilst MAJESTIC ANTHEM lost its right front plate in the middle stages.\nPassing the 200 Metres, GOAL FOR GOLD was steadied when awkwardly placed outside the heels of CHOICE TREASURE which was taken out by OBLITERATOR which shifted ground when tiring.\nFor a short distance near the 100 Metres, GOODHEART SUCCESS had some difficulty obtaining clear running.\nA veterinary inspection of OBLITERATOR immediately following the race did not show any significant findings.\nMAJESTIC ANTHEM and HIGH AND MIGHTY were sent for sampling.\n<19/11/2015 Additional Veterinary Report>The Clinical Veterinary Surgeon reported that BULLISH BOY was lame in its left front leg on the morning after racing. Before being allowed to race again, BULLISH BOY will be subjected to an official veterinary examination.\n1010.0Autumn4.9073336.3569740531
finishing_positionhorse_numberhorse_namehorse_idjockeytraineractual_weightdeclared_horse_weightdrawlength_behind_winnerrunning_position_1running_position_2running_position_3running_position_4finish_timewin_oddsrunning_position_5running_position_6race_idsrcrace_daterace_courserace_numberrace_classrace_distancetrack_conditionrace_nametracksectional_timeincident_reportrecent_6_runsrecent_ave_rankSeasonjockey_ave_ranktrainer_ave_rankrace_course_idtrack_idseason_idtrack_condition_id
22226412.0STRATHSPEYA271C MurrayA T Millard1171157103-3/48.08.04.0NaN1.10.5549NaNNaN2016-77920170709-1.html2017-07-09Sha Tin1Griffin Race1200GOODCHAI WAN ROAD PLATETURF - "B+2" COURSE23.93 22.98 23.06\n Whilst in the saddling stalls, ELEGANCE PROMISE had its right front plate refitted. ELEGANCE PROMISE was examined by the Veterinary Officer who said in his opinion it was suitable to race.\nFANTASTIC SHOW began only fairly.\nCHATER THUNDER began awkwardly and then was steadied shortly after the start when crowded for room between BINGO which was taken out by RIVERSIDE BIRD and ELEGANCE PROMISE which was taken in by GALLANT RETURN which in turn raced tight inside LUCKY MASTER. In this incident, ELEGANCE PROMISE became badly unbalanced after being bumped on the hindquarters by CHATER THUNDER.\nFrom wide barriers, TELECOM SUN and STRATHSPEY were steadied in the early stages and shifted across behind runners.\nApproaching the 1000 Metres, CHATER THUNDER momentarily raced tight between BINGO and LUCKY MASTER which shifted in.\nWhen racing wide, CHAPARRAL STAR was steadied approaching and passing the 1000 Metres to obtain cover.\nMaking the turn after the 900 Metres, CHATER THUNDER got its head on the side and lay out towards the heels of GALLANT RETURN.\nApproaching the 600 Metres, LUCKY STRYKER was awkwardly placed close to the heels of LUCKY MASTER.\nNear the 550 Metres, STAR OF HONG KONG raced in restricted room inside FANTASTIC SHOW which improved into tight running inside CHAPARRAL STAR.\nRounding the Home Turn, CHAPARRAL STAR was taken wider by FANTASTIC SHOW which shifted out to improve.\nAt the entrance to the Straight, STRATHSPEY was awkwardly placed close to the heels of CHATER THUNDER.\nPassing the 200 Metres, STAR OF HONG KONG was shifted in away from the heels of ELEGANCE PROMISE which was weakening.\nELEGANCE PROMISE and TELECOM SUN bumped near the 150 Metres.\nOver the concluding stages, STRATHSPEY raced tight inside BINGO which shifted in after being awkwardly placed inside GALLANT RETURN.\nWhen questioned regarding the performance of LUCKY MASTER, Apprentice H N Wong stated that he was asked to try and lead on LUCKY MASTER. He said, unlike at its last start, the pace of the race in the early stages was quick and when a number of horses which were better drawn than LUCKY MASTER were hard ridden to obtain forward positions, he elected to steady LUCKY MASTER and obtain a trailing position passing the 1100 Metres. He said LUCKY MASTER travelled well behind runners, however, in the Straight finished off the race only one-paced. He added whilst LUCKY MASTER may have appeared disappointing, the circumstances of the race, in particular the tempo in the early stages, were very different from that of last start when the horse was able to obtain an uncontested lead. A veterinary inspection of LUCKY MASTER immediately following the race did not show any significant findings.\nLUCKY MASTER, FANTASTIC SHOW and GALLANT RETURN were sent for sampling.\n<10/7/2017 Additional Veterinary Report>LUCKY MASTER, which performed poorly, was examined by the Veterinary Officer who said at that time there were no significant findings. LUCKY MASTER was again examined by the Veterinary Officer at the stables of Trainer R Gibson this morning. He said at this time he noted the horse to be lame in its left front leg. Before being allowed to race again, LUCKY MASTER will be subjected to an official veterinary examination.\n44.0Summer6.9146346.2848610641
2222792.0CHATER THUNDERA325N CallanD E Ferraris1261041485.06.09.0NaN1.11.2629NaNNaN2016-77920170709-1.html2017-07-09Sha Tin1Griffin Race1200GOODCHAI WAN ROAD PLATETURF - "B+2" COURSE23.93 22.98 23.06\n Whilst in the saddling stalls, ELEGANCE PROMISE had its right front plate refitted. ELEGANCE PROMISE was examined by the Veterinary Officer who said in his opinion it was suitable to race.\nFANTASTIC SHOW began only fairly.\nCHATER THUNDER began awkwardly and then was steadied shortly after the start when crowded for room between BINGO which was taken out by RIVERSIDE BIRD and ELEGANCE PROMISE which was taken in by GALLANT RETURN which in turn raced tight inside LUCKY MASTER. In this incident, ELEGANCE PROMISE became badly unbalanced after being bumped on the hindquarters by CHATER THUNDER.\nFrom wide barriers, TELECOM SUN and STRATHSPEY were steadied in the early stages and shifted across behind runners.\nApproaching the 1000 Metres, CHATER THUNDER momentarily raced tight between BINGO and LUCKY MASTER which shifted in.\nWhen racing wide, CHAPARRAL STAR was steadied approaching and passing the 1000 Metres to obtain cover.\nMaking the turn after the 900 Metres, CHATER THUNDER got its head on the side and lay out towards the heels of GALLANT RETURN.\nApproaching the 600 Metres, LUCKY STRYKER was awkwardly placed close to the heels of LUCKY MASTER.\nNear the 550 Metres, STAR OF HONG KONG raced in restricted room inside FANTASTIC SHOW which improved into tight running inside CHAPARRAL STAR.\nRounding the Home Turn, CHAPARRAL STAR was taken wider by FANTASTIC SHOW which shifted out to improve.\nAt the entrance to the Straight, STRATHSPEY was awkwardly placed close to the heels of CHATER THUNDER.\nPassing the 200 Metres, STAR OF HONG KONG was shifted in away from the heels of ELEGANCE PROMISE which was weakening.\nELEGANCE PROMISE and TELECOM SUN bumped near the 150 Metres.\nOver the concluding stages, STRATHSPEY raced tight inside BINGO which shifted in after being awkwardly placed inside GALLANT RETURN.\nWhen questioned regarding the performance of LUCKY MASTER, Apprentice H N Wong stated that he was asked to try and lead on LUCKY MASTER. He said, unlike at its last start, the pace of the race in the early stages was quick and when a number of horses which were better drawn than LUCKY MASTER were hard ridden to obtain forward positions, he elected to steady LUCKY MASTER and obtain a trailing position passing the 1100 Metres. He said LUCKY MASTER travelled well behind runners, however, in the Straight finished off the race only one-paced. He added whilst LUCKY MASTER may have appeared disappointing, the circumstances of the race, in particular the tempo in the early stages, were very different from that of last start when the horse was able to obtain an uncontested lead. A veterinary inspection of LUCKY MASTER immediately following the race did not show any significant findings.\nLUCKY MASTER, FANTASTIC SHOW and GALLANT RETURN were sent for sampling.\n<10/7/2017 Additional Veterinary Report>LUCKY MASTER, which performed poorly, was examined by the Veterinary Officer who said at that time there were no significant findings. LUCKY MASTER was again examined by the Veterinary Officer at the stables of Trainer R Gibson this morning. He said at this time he noted the horse to be lame in its left front leg. Before being allowed to race again, LUCKY MASTER will be subjected to an official veterinary examination.\n99.0Summer6.072336.2750260641
22228106.0STAR OF HONG KONGA019A SannaC W Chang126114099-1/210.011.010.0NaN1.11.4999NaNNaN2016-77920170709-1.html2017-07-09Sha Tin1Griffin Race1200GOODCHAI WAN ROAD PLATETURF - "B+2" COURSE23.93 22.98 23.06\n Whilst in the saddling stalls, ELEGANCE PROMISE had its right front plate refitted. ELEGANCE PROMISE was examined by the Veterinary Officer who said in his opinion it was suitable to race.\nFANTASTIC SHOW began only fairly.\nCHATER THUNDER began awkwardly and then was steadied shortly after the start when crowded for room between BINGO which was taken out by RIVERSIDE BIRD and ELEGANCE PROMISE which was taken in by GALLANT RETURN which in turn raced tight inside LUCKY MASTER. In this incident, ELEGANCE PROMISE became badly unbalanced after being bumped on the hindquarters by CHATER THUNDER.\nFrom wide barriers, TELECOM SUN and STRATHSPEY were steadied in the early stages and shifted across behind runners.\nApproaching the 1000 Metres, CHATER THUNDER momentarily raced tight between BINGO and LUCKY MASTER which shifted in.\nWhen racing wide, CHAPARRAL STAR was steadied approaching and passing the 1000 Metres to obtain cover.\nMaking the turn after the 900 Metres, CHATER THUNDER got its head on the side and lay out towards the heels of GALLANT RETURN.\nApproaching the 600 Metres, LUCKY STRYKER was awkwardly placed close to the heels of LUCKY MASTER.\nNear the 550 Metres, STAR OF HONG KONG raced in restricted room inside FANTASTIC SHOW which improved into tight running inside CHAPARRAL STAR.\nRounding the Home Turn, CHAPARRAL STAR was taken wider by FANTASTIC SHOW which shifted out to improve.\nAt the entrance to the Straight, STRATHSPEY was awkwardly placed close to the heels of CHATER THUNDER.\nPassing the 200 Metres, STAR OF HONG KONG was shifted in away from the heels of ELEGANCE PROMISE which was weakening.\nELEGANCE PROMISE and TELECOM SUN bumped near the 150 Metres.\nOver the concluding stages, STRATHSPEY raced tight inside BINGO which shifted in after being awkwardly placed inside GALLANT RETURN.\nWhen questioned regarding the performance of LUCKY MASTER, Apprentice H N Wong stated that he was asked to try and lead on LUCKY MASTER. He said, unlike at its last start, the pace of the race in the early stages was quick and when a number of horses which were better drawn than LUCKY MASTER were hard ridden to obtain forward positions, he elected to steady LUCKY MASTER and obtain a trailing position passing the 1100 Metres. He said LUCKY MASTER travelled well behind runners, however, in the Straight finished off the race only one-paced. He added whilst LUCKY MASTER may have appeared disappointing, the circumstances of the race, in particular the tempo in the early stages, were very different from that of last start when the horse was able to obtain an uncontested lead. A veterinary inspection of LUCKY MASTER immediately following the race did not show any significant findings.\nLUCKY MASTER, FANTASTIC SHOW and GALLANT RETURN were sent for sampling.\n<10/7/2017 Additional Veterinary Report>LUCKY MASTER, which performed poorly, was examined by the Veterinary Officer who said at that time there were no significant findings. LUCKY MASTER was again examined by the Veterinary Officer at the stables of Trainer R Gibson this morning. He said at this time he noted the horse to be lame in its left front leg. Before being allowed to race again, LUCKY MASTER will be subjected to an official veterinary examination.\n1010.0Summer7.3076927.0607530641
2222914.0COBY BOYA297M L YeungT P Yung12411516-8.07.05.01.01.21.4198NaNNaN2016-78820170709-10.html2017-07-09Sha Tin10Class 31400GOODLEI YUE MUN PARK HANDICAPTURF - "B+2" COURSE13.29 21.64 22.98 23.50\n RADIANT BUNNY was withdrawn on 8.7.17 by order of the Stewards acting on veterinary advice (lame right fore) and was replaced by Standby Declared Starter LUCKY GUY (N Callan). Before being allowed to race again, RADIANT BUNNY will be subjected to an official veterinary examination.\nC Y Ho (ALCAZAR) was fined the sum of $2,000 for presenting himself to be weighed out with the incorrect saddle cloth number.\nMAGIC AGILITY was slow to begin.\nREGENCY BO BO shifted in at the start, resulting in DEJA VU being crowded for room between that horse and STARLIT KNIGHT. In this incident, STARLIT KNIGHT became unbalanced when bumped by DEJA VU.\nCOBY BOY shifted out at the start and inconvenienced GRANITE BELT.\nWhen being steadied from a wide barrier shortly after the start, ALCAZAR got its head up.\nFrom wide barriers, GIANT TURTLE and IRON BOY were shifted across behind runners in the early stages.\nApproaching the 1100 Metres, LUCKY GUY was awkwardly placed close to the heels of STARLIT KNIGHT which shifted out.\nPassing the 400 Metres, DEJA VU was shifted out in an attempt to improve between PRESIDENTPARAMOUNT and COBY BOY and in doing so bumped the hindquarters of COBY BOY, resulting in that horse becoming unbalanced and shifting in. DEJA VU was consequently disappointed for running between those horses and approaching the 300 Metres was shifted to the outside of COBY BOY.\nApproaching the 200 Metres, FLYING MONKEY was hampered when disappointed for running between SIR REDALOT and REGENCY BO BO, both of which shifted slight ground.\nPassing the 100 Metres, DEJA VU was awkwardly placed behind COBY BOY when racing tight inside LUCKY GUY.\nThroughout the race, PRESIDENTPARAMOUNT travelled wide and without cover.\nThe performances of MAGIC AGILITY and ALCAZAR, which finished tailed out, were considered unacceptable. Before being allowed to race again, MAGIC AGILITY and ALCAZAR will be required to perform to the satisfaction of the Stewards in a barrier trial and be subjected to an official veterinary examination.\nAfter the race, J Moreira stated that he had to make too much use of SIR REDALOT in the early stages to cross to the lead and consequently the horse was then not able to finish off the race strongly. A veterinary inspection of SIR REDALOT immediately following the race did not show any significant findings.\nREGENCY BO BO, COBY BOY and LUCKY GUY were sent for sampling.\n1/11.0Summer6.9783466.3466960641
22230105.0A STAR LUSTERA259K C NgK L Man1241133105-3/412.010.010.0NaN0.58.2899NaNNaN2016-78920170712-1.html2017-07-12Happy Valley1Class 41000GOOD TO FIRMBULLDOZER HANDICAPTURF - "A" COURSE12.57 21.25 23.54\n A STAR LUSTER began awkwardly and lost ground and then raced greenly throughout the event.\nDR RACE was slow to begin and then was hampered by TRENDIFUL which was taken out by YOU KNOW WHO which, after beginning awkwardly, got its head on the side and shifted out further despite the efforts of its rider.\nRUGBY DIAMOND shifted out on jumping and hampered TRIUMPHAL TRUMPET.\nFrom the outside barrier, SNOW HAWK was steadied in the early stages and shifted across behind runners.\nIn the early and middle stages, TRIUMPHAL TRUMPET raced greenly.\nC Murray (DOUBLE MASTER) pleaded guilty to a charge of careless riding [Rule 100(1)] in that near the 850 Metres he permitted his mount to shift in when not clear of ACCESSION YEARS, causing that horse to be hampered and taken in and ultimately to be steadied away from the heels of DOUBLE MASTER. C Murray was suspended from riding in races for a period to commence on Monday, 17 July 2017 and to expire on Thursday, 27 July 2017 on which day he may resume race riding.\nApproaching the 800 Metres, TRENDIFUL was momentarily awkwardly placed close to the heels of DOUBLE MASTER.\nPassing the 800 Metres, SILVER SPUN was steadied away from the heels of RUGBY DIAMOND (C Schofield) which shifted in when not properly clear. C Schofield was severely reprimanded and advised to ensure that he is properly clear when shifting ground in similar circumstances.\nAfter the 800 Metres, ACCESSION YEARS was left racing wide and without cover and making the turn near the 550 Metres got its head on the side, lay in and brushed TRENDIFUL which became unbalanced. TRENDIFUL then hung out, resulting in ACCESSION YEARS being taken wider and both horses then racing wide and without cover.\nAlso making the turn near the 550 Metres, SILVER SPUN became unbalanced after being bumped by OCEAN ROAR which made the turn awkwardly.\nNear the 450 Metres, CALIFORNIA ASPAR was awkwardly placed close to the heels of OCEAN ROAR when racing tight inside DOUBLE MASTER.\nPassing the 200 Metres, DR RACE drifted out under pressure.\nA veterinary inspection of OCEAN ROAR immediately following the race including an endoscopic examination showed a substantial amount of mucus in the horse’s trachea.\nDR RACE, SILVER SPUN and CALIFORNIA ASPAR were sent for sampling.\n10/1211.0Summer8.1186996.7722281142
22231126.0SNOW HAWKA289C Y HoC Fownes12510261211-1/210.012.012.0NaN0.59.2299NaNNaN2016-78920170712-1.html2017-07-12Happy Valley1Class 41000GOOD TO FIRMBULLDOZER HANDICAPTURF - "A" COURSE12.57 21.25 23.54\n A STAR LUSTER began awkwardly and lost ground and then raced greenly throughout the event.\nDR RACE was slow to begin and then was hampered by TRENDIFUL which was taken out by YOU KNOW WHO which, after beginning awkwardly, got its head on the side and shifted out further despite the efforts of its rider.\nRUGBY DIAMOND shifted out on jumping and hampered TRIUMPHAL TRUMPET.\nFrom the outside barrier, SNOW HAWK was steadied in the early stages and shifted across behind runners.\nIn the early and middle stages, TRIUMPHAL TRUMPET raced greenly.\nC Murray (DOUBLE MASTER) pleaded guilty to a charge of careless riding [Rule 100(1)] in that near the 850 Metres he permitted his mount to shift in when not clear of ACCESSION YEARS, causing that horse to be hampered and taken in and ultimately to be steadied away from the heels of DOUBLE MASTER. C Murray was suspended from riding in races for a period to commence on Monday, 17 July 2017 and to expire on Thursday, 27 July 2017 on which day he may resume race riding.\nApproaching the 800 Metres, TRENDIFUL was momentarily awkwardly placed close to the heels of DOUBLE MASTER.\nPassing the 800 Metres, SILVER SPUN was steadied away from the heels of RUGBY DIAMOND (C Schofield) which shifted in when not properly clear. C Schofield was severely reprimanded and advised to ensure that he is properly clear when shifting ground in similar circumstances.\nAfter the 800 Metres, ACCESSION YEARS was left racing wide and without cover and making the turn near the 550 Metres got its head on the side, lay in and brushed TRENDIFUL which became unbalanced. TRENDIFUL then hung out, resulting in ACCESSION YEARS being taken wider and both horses then racing wide and without cover.\nAlso making the turn near the 550 Metres, SILVER SPUN became unbalanced after being bumped by OCEAN ROAR which made the turn awkwardly.\nNear the 450 Metres, CALIFORNIA ASPAR was awkwardly placed close to the heels of OCEAN ROAR when racing tight inside DOUBLE MASTER.\nPassing the 200 Metres, DR RACE drifted out under pressure.\nA veterinary inspection of OCEAN ROAR immediately following the race including an endoscopic examination showed a substantial amount of mucus in the horse’s trachea.\nDR RACE, SILVER SPUN and CALIFORNIA ASPAR were sent for sampling.\n12/1011.0Summer7.0281056.3569741142
22232113.0BIG TIME BABYA157C MurrayK L Man13111095510.010.011.0NaN0.57.9099NaNNaN2016-79320170712-5.html2017-07-12Happy Valley5Class 31000GOOD TO FIRMLET ME FIGHT HANDICAPTURF - "A" COURSE12.67 20.97 23.44\n TRAVEL COMFORTS was withdrawn on 10.7.17 by order of the Stewards acting on veterinary advice (lame left hind) and was replaced by Standby Declared Starter ALL MY GAIN. Before being allowed to race again, TRAVEL COMFORTS will be subjected to an official veterinary examination. \nRACING MATE and BIG TIME BABY began only fairly and shortly after the start were crowded for room outside SWEETIE BARLEY which shifted out.\nTRIUMPHANT JEWEL and MOST BEAUTIFUL bumped at the start.\nFrom a wide barrier, ALL MY GAIN was shifted across behind runners in the early stages.\nPassing the 950 Metres, BEAUTY MASTER shifted out abruptly.\nApproaching the 900 Metres, MONEY BOY shifted in and bumped the hindquarters of WHO ELSE BUT YOU.\nBIG TIME BABY lost its left front plate passing the 800 Metres.\nMaking the turn near the 550 Metres, BEAUTY MASTER got its head on the side and lay out.\nWhen questioned, K Teetan (ACE KING) stated that he was instructed to attempt to lead if ACE KING began well having regard to the horse’s prior history of being fractious in the barriers. He said he was also told that if ACE KING did not show sufficient early speed to lead, it was acceptable for him to take a position with cover behind the leaders. He said, as is often the case, ACE KING was fractious in the barriers and reared and then as the start was effected began only fairly. He said as there were a number of horses, which were drawn better than ACE KING, being vigorously ridden to obtain forward positions, he did not believe that ACE KING would have sufficient early speed to be able to lead, therefore he elected to shift the horse across and obtain cover albeit further back in the field than had been intended.\nTRIUMPHANT JEWEL and MONEY BOY were sent for sampling.\n11/910.0Summer6.9146346.7722281142
2223317.0SOUTHERN LEGENDA252K TeetanC Fownes118112212-8.08.01.0NaN1.09.5518NaNNaN2016-79620170712-8.html2017-07-12Happy Valley8Class 21200GOOD TO FIRMSWEET ORANGE HANDICAPTURF - "A" COURSE23.51 22.62 23.42\n DRAGON GENERAL was slow to begin.\nFrom a wide barrier, SUPER TURBO was steadied in the early stages and shifted across behind runners.\nNear the 1150 Metres, COUNTRY MELODY and VERBINSKY were crowded for room inside HIGH FIVE (C Murray) which shifted in before being directed back out to relieve the tightening to runners on its inside. C Murray was advised to exercise more care when shifting ground.\nThroughout the race, FLYING TOURBILLON travelled wide and without cover.\nA veterinary inspection of RACING SUPERNOVA, HOUSE OF FUN and DRAGON GENERAL immediately following the race did not show any significant findings.\nSOUTHERN LEGEND and SUPER TURBO were sent for sampling.\n1/11.0Summer6.3070576.3569741142
22234118.0SO GENEROUSV402A SannaC H Yip1259756810.011.011.0NaN1.11.3599NaNNaN2016-79920170716-3.html2017-07-16Sha Tin3Class 41200GOODBIG PROFIT HANDICAPTURF - "C" COURSE23.54 22.53 24.01\n VERY RICH MAN was withdrawn on 15.7.17 by order of the Stewards acting on veterinary advice (lame right fore) and was replaced by first Standby Declared Starter JOLLY BOUNTIFUL (J Moreira). AIMING HIGH was also withdrawn on 15.7.17 by order of the Stewards acting on veterinary advice (lame right fore) and was replaced by second Standby Declared Starter RICHCITY FORTUNE (B Prebble). Before being allowed to race again, VERY RICH MAN and AIMING HIGH will be subjected to an official veterinary examination.\nSO GENEROUS began only fairly.\nFrom the outside barriers, GREAT TOPLIGHT and MY GIFT were steadied and shifted across behind runners in the early stages.\nNear the 750 Metres, JOLLY BOUNTIFUL was awkwardly placed close to the heels of OUR FOLKS.\nPassing the 400 Metres, JOLLY BOUNTIFUL was directed in from behind PHANTOM FALCON and OUR FOLKS to obtain clear running.\nJOLLY BOUNTIFUL had difficulty obtaining clear running from passing the 200 Metres and approaching the 150 Metres was shifted out away from the heels of RUMINARE. JOLLY BOUNTIFUL was then badly held up when disappointed for running between RUMINARE and RICHCITY FORTUNE. Then passing the 100 Metres, JOLLY BOUNTIFUL was shifted to the outside of RICHCITY FORTUNE to obtain clear running.\nJOLLY BOUNTIFUL, OUR FOLKS and PHANTOM FALCON were sent for sampling.\n11/1111.0Summer7.3076926.8235770241
22235111.0CALCULATIONA248J MoreiraJ Size12010767-1.01.01.01.01.23.094.6NaNNaN2016-80520170716-9.html2017-07-16Sha Tin9Class 31400GOOD TO YIELDINGMR AWARD HANDICAPTURF - "C" COURSE13.43 22.23 24.01 23.42\n WINSTON’S LAD was withdrawn on 13.7.17 by order of the Stewards acting on veterinary advice (swollen left front fetlock) and was replaced by Standby Declared Starter MIGHTY BOY. Before being allowed to race again, WINSTON’S LAD will be subjected to an official veterinary examination.\nWhen parading prior to the race, the tongue tie applied to DOUBLE VALENTINE became dislodged with this gear being reapplied behind the barriers.\nJust prior to the start being effected, HEHA BOY became very fractious and lunged at the front gates, resulting in its rider, Apprentice H N Wong, being dislodged and HEHA BOY leaving the barrier stalls riderless. Before being allowed to race again, HEHA BOY will be required to perform satisfactorily in a barrier trial.\nHIGH AND MIGHTY was very slow to begin. A veterinary inspection of HIGH AND MIGHTY immediately following the race found that horse to be lame in its right front leg, however, the horse was unable to be scoped due to being fractious. Having regard to HIGH AND MIGHTY’s previous record of having been slow to begin, the horse will be required to perform satisfactorily in a barrier trial and be subjected to an official veterinary examination before being allowed to race again.\nSUNNY WAY began very awkwardly and shifted in abruptly, resulting in DOUBLE VALENTINE being badly hampered.\nMIGHTY BOY and FANTASTIC KAKA began only fairly.\nPassing the 1200 Metres, MIGHTY BOY was momentarily crowded for room inside INTREPIC (Apprentice M F Poon) which shifted in before being directed back out to relieve the tightening to MIGHTY BOY. Apprentice Poon was advised to exercise more care when shifting ground in similar circumstances.\nApproaching the 1000 Metres, CHUNG WAH SPIRIT was awkwardly placed close to the heels of WORLD RECORD and shifted out away from the heels of that horse. SUNNY WAY, which was racing to the outside of CHUNG WAH SPIRIT, was hampered in consequence.\nNear the 250 Metres, CHUNG WAH SPIRIT commenced to shift in under pressure, resulting in its rider, Z Purton, momentarily having to stop riding and straighten his mount.\nPassing the 100 Metres, WORLD RECORD was shifted in away from the heels of SUNNY WAY which lay in under pressure.\nThroughout the race, SUNNY WAY travelled wide and without cover.\nS Clipperton pleaded guilty to a breach of Rule 100(2) in that he failed to ride SUNNY WAY, 6th placegetter, out all the way to the end of the race to the satisfaction of the Stewards. S Clipperton was fined $15,000.\nCHUNG WAH SPIRIT lost its right front plate after the race.\nWhen questioned regarding the performance of INTREPIC, Apprentice M F Poon stated that the horse travelled well in the early and middle stages and rounding the Home Turn he anticipated the horse would finish off the race strongly after being shifted to the outside of RIGHT HONOURABLE. He said however that passing the 400 Metres INTREPIC did not quicken as it had when ridden by him in the past. He added after this INTREPIC did not finish off the race as he expected and was disappointing over the concluding stages. A veterinary inspection of INTREPIC immediately following the race did not show any significant findings.\nA veterinary inspection of WORLD RECORD immediately following the race did not show any significant findings.\nINTREPIC, CALCULATION and CHUNG WAH SPIRIT were sent for sampling.\n<17/7/2017 Additional Veterinary Report>WORLD RECORD, which performed poorly, was examined by the Veterinary Officer who said at that time there were no significant findings. WORLD RECORD was again examined by the Veterinary Officer at the stables of Trainer A T Millard this morning. He said at this time he noted the horse to be lame in its right front leg. Before being allowed to race again, WORLD RECORD will be subjected to an official veterinary examination.\n1/11.0Summer4.044985.0716160243